CN108226693B - Method and apparatus for detecting short circuit in battery in real time, and computer-readable storage medium - Google Patents

Method and apparatus for detecting short circuit in battery in real time, and computer-readable storage medium Download PDF

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CN108226693B
CN108226693B CN201711364956.XA CN201711364956A CN108226693B CN 108226693 B CN108226693 B CN 108226693B CN 201711364956 A CN201711364956 A CN 201711364956A CN 108226693 B CN108226693 B CN 108226693B
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temperature
parameter
value
data
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CN108226693A (en
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冯旭宁
潘岳
李世超
欧阳明高
卢艳华
何向明
卢兰光
王莉
郑思奇
陈天雨
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Tsinghua University
Contemporary Amperex Technology Co Ltd
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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
    • 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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm

Abstract

The invention discloses a real-time battery internal short circuit detection method, a real-time battery internal short circuit detection device and a computer readable storage medium. The real-time battery internal short circuit detection method comprises the following steps: s10, acquiring battery temperature data and battery current data of the battery; s20, calculating a battery standard value according to the battery temperature data and the battery current data; s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value; and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value. The real-time battery internal short circuit detection method can obtain a thermal parameter comparison value according to a battery thermal parameter identification standard formula, judge whether the battery generates heat abnormally or not according to the comparison value in real time, and is accurate and efficient.

Description

Method and apparatus for detecting short circuit in battery in real time, and computer-readable storage medium
Technical Field
The present invention relates to the field of battery detection, and in particular, to a real-time battery internal short circuit detection method, a real-time battery internal short circuit detection apparatus, and a computer-readable storage medium.
Background
When the conventional lithium ion battery is used as a power battery for a vehicle, a working failure or a safety problem may occur. One of the common safety issues is short circuiting within the cell. The internal short circuit of the lithium ion power battery generally refers to the phenomenon that abnormal discharge and abnormal heat generation are caused by the generation of a current loop in the power battery. Abnormal heat generation of the internal short circuit may cause the power battery to be in danger of thermal runaway, fire, explosion and the like.
Therefore, short circuits in the power cells must be effectively prevented and controlled. The most direct method is internal short detection. The traditional method for detecting the short circuit in the battery can only detect the battery in a non-working state, but cannot accurately detect the condition of vehicle-mounted installation of the battery after leaving factory and the driving working condition of external load/current output.
Disclosure of Invention
Therefore, it is necessary to provide a real-time battery internal short circuit detection method, a real-time battery internal short circuit detection device, and a computer-readable storage medium, for solving the problem that the conventional battery internal short circuit detection method cannot detect the driving condition.
A real-time battery internal short circuit detection method comprises the following steps:
s10, acquiring battery temperature data and battery current data of the battery;
s20, calculating a battery standard value according to the battery temperature data and the battery current data;
s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value;
and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value.
In one embodiment, the battery standard value includes a battery average temperature value and a battery maximum temperature value, and the step S20 includes:
s210, averaging according to the battery temperature data to obtain the average temperature value T of the batteryavg；
S220, selecting the maximum value in the battery temperature data as the temperatureMaximum temperature value T of the batterymax。
In one embodiment, the battery thermal parameters include a battery equivalent heat generation internal resistance parameter and a battery entropy change heat generation parameter, and the step S30 includes:
s310, acquiring battery experiment temperature data and battery experiment current data;
s320, calculating the thermal parameter discrimination standard formula of the battery according to the battery temperature experimental data and the battery experimental current data of the battery and a battery thermal model, wherein the battery thermal model is as follows:
wherein M is the mass of the battery and the unit is kg; cpThe specific heat capacity of the battery is J.kg-1·K-1；Is the derivative of the battery temperature T with respect to time, and has the unit ℃ · s-1(ii) a h is the average heat transfer coefficient of the battery to the environment and has the unit of W.m2·K-1(ii) a A is the average heat dissipation area of the battery, and the unit is m2(ii) a T is a battery temperature value and the unit is; t is∞Is ambient temperature in units of; i is the battery current value, and the unit is A; rΩRepresenting the equivalent heat generation internal resistance parameter of the battery, and the unit is omega; t isKThe temperature of the battery is converted into temperature in Kelvin and is expressed in K and TK＝T+273.15；UTRepresenting the parameter of heat production of the battery entropy change with the unit of V.K-1。
In one embodiment, before the step S320, the method further includes:
and S311, based on the battery heat generation model, carrying out noise reduction processing on the battery temperature experimental data and the battery experimental current data.
In one embodiment, the method of noise reduction processing includes a recursive filtering method with a forgetting factor.
In one embodiment, the battery temperature experiment data includes the battery temperature values T acquired at equal intervals, and the battery experiment current data includes the battery current values I acquired at equal intervals.
In one embodiment, the thermal parameter comparison value comprises a thermal parameter R in average equivalent heat generation of the batteryΩ,avgBattery average entropy variation heat production parameter UTInternal resistance parameter R in worst equivalent heat production of batteryΩ,maxWorst entropy change heat production parameter U of sum cellT,maxAfter the step S320, the method further includes:
step S330, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the equivalent heat generation internal resistance parameter R of the batteryΩRespectively calculating the average equivalent heat production internal thermal resistance parameter R of the batteryΩ,avgAnd a resistance to heat parameter R within worst case equivalent heat production of said batteryΩ,max；
Step S340, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the battery entropy change heat production parameter UTRespectively calculating the average entropy variation heat production parameter U of the batteryT,avgThe worst entropy change heat generation parameter U of the batteryT,max。
In one embodiment, the step S40 includes:
s410, obtaining abnormal heat production factor Y through the comparison valueT；
S420 combining the normal heat production factor YTWith a predetermined threshold value ΛTThe comparison determines whether the battery pack abnormally generates heat.
In one embodiment, the abnormal thermogenic factor YTBy passing
Figure BDA0001512672540000031
And (4) obtaining.
In one embodiment, the preset threshold value Λ isTIncluding at least a first anomaly threshold value LambdaT1And a second anomaly threshold value ΛT2Said second anomaly threshold value ΛT2Is greater than the first anomaly threshold value lambdaT1The exception level of (2).
A real-time in-cell short detection apparatus, comprising a real-time in-cell short detection device and a computer, wherein the computer comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program by using a real-time in-cell short detection method, the method comprising:
s10, acquiring battery temperature data and battery current data of the battery;
s20, calculating a battery standard value according to the battery temperature data and the battery current data;
s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value;
and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is operative to carry out the steps of the method.
The invention provides a real-time battery inner short circuit detection method, which comprises the following steps: s10, acquiring battery temperature data and battery current data of the battery; s20, calculating a battery standard value according to the battery temperature data and the battery current data; s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value; and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value. The real-time battery internal short circuit detection method can obtain a thermal parameter comparison value according to a battery thermal parameter identification standard formula, judge whether the battery generates heat abnormally or not according to the comparison value in real time, and is accurate and efficient.
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Fig. 1 is a flowchart of a real-time battery internal short circuit detection method according to an embodiment of the present invention;
fig. 2 is a diagram illustrating an effect of recursive filtering in the real-time battery internal short circuit detection method according to the embodiment of the present invention;
fig. 3 is a schematic diagram illustrating an abnormal decrease in voltage and an abnormal increase in temperature of an internal short-circuited battery according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a thermal parameter identification result of a battery based on a heat generation model according to an embodiment of the present invention;
FIG. 5 shows an abnormal factor Y according to an embodiment of the present inventionTA schematic of the classification;
fig. 6 is a structural diagram of a real-time battery internal short circuit detection device according to an embodiment of the present invention;
description of reference numerals:
real-time in-cell short circuit detection apparatus 10, real-time in-cell short circuit detection apparatus 11, computer 12, memory 100, processor 200, computer program 300
Detailed Description
In order to make the objects, technical solutions and technical effects of the present invention more apparent, specific embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, an embodiment of the present invention provides a real-time battery internal short circuit detection method, including:
s10, acquiring battery temperature data and battery current data of the battery;
s20, calculating a battery standard value according to the battery temperature data and the battery current data;
s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value;
and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value.
In step S10, the battery temperature data and the battery current data may be battery temperature data information and battery current data collected at equal intervals during driving. A battery management system of a vehicle may control a plurality of the battery-controlled battery packs. The battery temperature data and the battery current data may be collected by a battery management system. The vehicle battery management system may collect the battery temperature data, the battery current data of each battery in the battery pack.
In step S20, the battery criterion value may be a value obtained by calculation processing of the battery temperature data and the battery current data, and capable of reflecting the overall characteristics of the battery temperature data and the battery current data. In one embodiment, the battery standard value may be a temperature average value of the battery, a current average value of the battery, which is calculated by the battery temperature data and the battery current data. The battery standard value may also be a median battery temperature value, a median battery current value, or the like calculated from the battery temperature data and the battery current data.
In step 30, the battery thermal parameter may be a thermal parameter in a battery heat generation model. The battery thermal parameter may be plural. The battery thermal parameter discrimination standard can be obtained by a battery heat generation model. And substituting the battery standard value into the battery thermal parameter identification standard formula to calculate the thermal parameter comparison value.
In step S40, the thermal parameter comparison value may be calculated to obtain a calculation result for determining the battery operating condition. It is possible to determine whether the battery abnormally generates heat according to the calculation result. In one embodiment, whether the battery abnormally generates heat may be determined by comparing the calculation result with a preset empirical value. Further, the preset empirical value may be a plurality of empirical values having different risk levels.
The invention can acquire the battery temperature data and the battery current data of the battery in real time; calculating a battery standard value in real time according to the battery temperature data acquired in real time and the battery current data acquired in real time; according to the battery standard value, a thermal parameter comparison value is obtained according to a battery thermal parameter identification standard formula; thereby determining whether the battery abnormally generates heat according to the thermal parameter comparison value. The real-time battery internal short circuit detection method can detect the heat generation condition of the battery in real time in the vehicle running process, and judges whether the battery has the internal short circuit condition according to the heat generation condition of the battery. The real-time battery internal short circuit detection method provides an effective scheme for internal short circuit fault detection under the running working condition of the power battery.
In one embodiment, the battery standard value comprises a battery average temperature value and a battery maximum temperature value. The step S20 includes:
s210, averaging according to the battery temperature data to obtain the average temperature value T of the batteryavg；
S220, selecting the maximum value in the battery temperature data as the maximum temperature value T of the batterymax。
The average temperature value T of the batteryavgCan be as follows:
wherein T isiThe current value collected for the ith time of a single battery in the interval time can be collected for N times.
The maximum temperature value T of the batterymaxCan be as follows:
Figure BDA0001512672540000061
wherein N is the number of times of collecting temperature values for a single battery, TiIs the temperature value acquired at the ith time.
In one embodiment, the battery thermal parameters include a battery equivalent heat generation internal resistance parameter and a battery entropy change heat generation parameter, and the step S30 includes:
s310, acquiring battery temperature experiment data and battery experiment current data;
s320, calculating the thermal parameter discrimination standard formula of the battery according to the battery temperature experimental data and the battery experimental current data of the battery and a battery thermal model, wherein the battery thermal model is as follows:
Figure BDA0001512672540000062
wherein M is the mass of the battery and the unit is kg; cpThe specific heat capacity of the battery is J.kg-1·K-1；
Figure BDA0001512672540000063
Is the derivative of the battery temperature T with respect to time, and has the unit ℃ · s-1(ii) a h is the average heat transfer coefficient of the battery to the environment and has the unit of W.m2·K-1(ii) a A is the average heat dissipation area of the battery, and the unit is m2(ii) a T is a battery temperature value and the unit is; t is∞Is ambient temperature in units of; i is the battery current value, and the unit is A; rΩRepresenting the equivalent heat generation internal resistance parameter of the battery, and the unit is omega; t isKThe temperature of the battery is converted into temperature in Kelvin and is expressed in K and TK＝T+273.15；UTRepresenting the parameter of heat production of the battery entropy change with the unit of V.K-1。
The parameter identification method based on the battery heat generation model satisfies equations (16) - (25).
Equation (16) is a basic equation of the model-based parameter identification method, where z represents an observed quantity, and according to the filtered model equation (15), in the problem of the present invention, z satisfies equation (17);
Figure BDA0001512672540000064
which is indicative of the amount of signal input,
Figure BDA0001512672540000065
is a column vector having two components
Figure BDA0001512672540000066
Andnamely, it is
Figure BDA0001512672540000068
Referring to the formula (15),
Figure BDA0001512672540000069
satisfies the formula (18),
Figure BDA00015126725400000610
satisfies formula (19); theta denotes the thermal parameter of the battery to be identified, theta also having two components theta1And theta2I.e. theta ═ theta1,θ2]TReference is made to the formula (15), θ1Satisfies the formula (20), theta2The formula (21) is satisfied.
Figure BDA00015126725400000611
Figure BDA0001512672540000071
Figure BDA0001512672540000072
θ1＝RΩ(20)
θ2＝UT(21)
Using the subscript k to denote the physical quantity corresponding to time k, e.g. zkRepresents the observed quantity at time k,
Figure BDA0001512672540000073
representing the observed quantity of the signal at time k, thetakIndicating the parameter identification result at the time k. z is a radical ofk，
Figure BDA0001512672540000074
And thetakThe formula (22) is satisfied.
Figure BDA0001512672540000075
But in fact, due to thetakIs obtained by parameter identification, and can only obtain theta through the parameter estimation value at the last momentk-1And z iskIs estimated value zk *：
Figure BDA0001512672540000076
Defining the estimation error epsilon at time kkComprises the following steps:
Figure BDA0001512672540000077
the time k parameter thetakThe recursive identification equation of (a) is:
Figure BDA0001512672540000078
wherein P iskFor recursively recognizing thetakA second order matrix of (a). PkThe diagonal symmetric matrix is positively determined for the fixed value by the steepest descent method. It can also be obtained by recursive least squares.
Through the equivalent heat production internal resistance parameter R of the batteryΩAnd the battery entropy change heat generation parameter UTCan reflect the intrinsic heat production information of the battery. Through the equivalent heat production internal resistance parameter R of the batteryΩAnd the battery entropy change heat generation parameter UTAnd is used to detect internal shorts.
In one embodiment, before the step S320, the method further includes:
and S311, based on the battery heat generation model, carrying out noise reduction processing on the battery temperature experimental data and the battery experimental current data. The noise reduction processing may include a clipping filtering method, a median filtering method, an arithmetic mean filtering method, and the like. The resolution of the battery temperature experimental data and the battery experimental current data can be improved by performing noise reduction processing on the battery temperature experimental data and the battery experimental current data, and the calculation precision and the calculation efficiency can be improved.
In one embodiment, the noise reduction processing method includes a recursive filtering method with a forgetting factor. The forgetting factor of the recursive filtering can be recorded as gamma, and the calculation process is as follows:
of the original dataThe derived vector is defined as { x }1，x2，x3，x4}. The data value at time k, i.e. x, is denoted by the subscript k1,kRepresenting the k time x1The numerical value of (c).
x1＝T (4)
x2＝T-T∞(5)
x3＝I2(6)
x4＝I·TK(7)
Performing recursive filtering with a forgetting factor gamma, wherein the vector after filtering is defined as y1，y2，y3，y4。y1，y2，y3，y4From { x1，x2，x3，x4Obtained after recursive filtering using the formulas (8) - (11), where the subscript k represents data at time k, and k-0 represents the initial time.
y1,0＝x1,0,y1,k＝γ·y1,k-1+x1,k,k＝1,2,3...N (8)
y2,0＝x2,0,y2,k＝γ·y2,k-1+x2,k,k＝1,2,3...N (9)
y3,0＝x3,0,y3,k＝γ·y3,k-1+x3,k,k＝1,2,3...N (10)
y4,0＝x4,0,y4,k＝γ·y4,k-1+x4,k,k＝1,2,3...N (11)
Filtered data y1Derivative of (2)
Figure BDA0001512672540000081
The calculation can be performed by equation (12), that is:
Figure BDA0001512672540000082
and is provided with
Figure BDA0001512672540000083
Namely, it is
Figure BDA0001512672540000084
Can reflect
Figure BDA0001512672540000085
The variation of (2). And:
Figure BDA0001512672540000086
then, since the signal before filtering satisfies:
Figure BDA0001512672540000091
i.e. the filtered signal may still satisfy the form of model equation (3).
Figure BDA0001512672540000092
Referring to fig. 2, since the actual sampling signal has noise and the temperature sampling resolution is low, the derivative of the battery temperature T with respect to time is directly calculated on lineThe data fluctuation is large, so that the calculation result of the next model-based parameter identification also fluctuates greatly, and a stable detection effect cannot be obtained. And after recursive filtering with forgetting factor, y is calculated1Derivative of (2)
Figure BDA0001512672540000094
The data is smoother, reflecting the real trend of the temperature rise rate of the battery changing along with the time.
In one embodiment, the battery temperature experiment data includes the battery temperature values T collected at equal time intervals. The battery experiment current data comprise the battery current value I acquired at equal time intervals.
In one embodiment, the thermal parameter comparison value comprises a batteryAverage equivalent heat production internal thermal parameter RΩ,avgBattery average entropy variation heat production parameter UTInternal resistance parameter R in worst equivalent heat production of batteryΩ,maxWorst entropy change heat production parameter U of sum cellT,maxAfter the step S320, the method further includes:
step S330, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the equivalent heat generation internal resistance parameter R of the batteryΩRespectively calculating the average equivalent heat production internal thermal resistance parameter R of the batteryΩ,avgAnd a resistance to heat parameter R within worst case equivalent heat production of said batteryΩ,max；
Step S340, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the battery entropy change heat production parameter UTRespectively calculating the average entropy variation heat production parameter U of the batteryT,avgThe worst entropy change heat generation parameter U of the batteryT,max。
In step S330, the average temperature value T of the battery is determinedavgThe maximum temperature value T of the batterymaxRespectively as T and T in formula (3)KSubstituting the equivalent heat generation internal resistance parameter R of the batteryΩRespectively obtaining the average equivalent heat production internal resistance parameter RΩ,avgAnd a resistance to heat parameter R within worst case equivalent heat production of said batteryΩ,max。
In step S340, the average temperature value T of the battery is determinedavgThe maximum temperature value T of the batterymaxRespectively as T and T in formula (3)KBringing into the battery entropy change heat production parameter UTRespectively calculating the average entropy variation heat production parameter U of the batteryT,avgThe worst entropy change heat generation parameter U of the batteryT,max。
In one embodiment, the step S40 includes:
s410, obtaining abnormal heat production factor Y through the comparison valueT；
S420 combining the normal heat production factor YTWith a predetermined threshold value ΛTThe comparison determines whether the battery pack abnormally generates heat.
In step S410, the abnormal heat generation factor YTThe abnormal heat generation condition of the current battery pack can be quantitatively evaluated, namely the uneven degree of heat generation of the battery pack can be quantitatively evaluated. Different threshold values Λ can be setTThe abnormal heat production degree is graded, and the rationality of comprehensively judging the abnormal heat production of the internal short circuit is improved. The abnormal thermogenic factor YTThe heat transfer parameter can be obtained by calculating the average equivalent heat production internal heat transfer parameter of the battery, the average entropy change heat production parameter of the battery, the worst equivalent heat production internal heat transfer parameter of the battery and the worst entropy change heat production parameter of the battery.
In one embodiment, the abnormal thermogenic factor YTBy passingAnd (4) obtaining. The abnormal thermogenic factor YTCan also pass through YT1＝|RΩ,max-RΩ,avg|+|UT,max-UT,avgAnd | obtaining.
In one embodiment, the preset threshold value Λ isTIncluding at least a first anomaly threshold value LambdaT1And a second anomaly threshold value ΛT2. The second anomaly threshold value ΛT2Is greater than the first anomaly threshold value lambdaT1The exception level of (2). In one embodiment, the first anomaly threshold value Λ isT1May be a reminder value that the battery has begun to produce heat. When the preset threshold value is lambdaTReaching the second anomaly threshold LambdaT2Then, it can be understood that the battery generates heat abnormally, and the use of the battery needs to be stopped.
In one embodiment, based on equation (3), M is 0.75kg, Cp＝1100J·kg-1·K-1，h＝15W·m2·K-1，A＝0.02m2. A plurality of batteries are connected in series to form a battery pack, wherein a certain battery internally comprises an internal short circuit controllable trigger element, and a more serious internal short circuit is triggered at 3598 s.
Referring to fig. 3, after the internal short circuit is triggered, the temperature in the battery pack abnormally rises. Rate of rise of abnormal cell temperature TmaxWell above the mean temperature TavgThe rate of rise of (c). Voltage V of abnormal unit cellminGradually deviating from the average voltage V of the battery packavg。
Referring to fig. 4, the battery average equivalent heat generation internal thermal parameter R of the battery obtained in the step S330 and the step S340 is identifiedΩ,avgAnd a resistance to heat parameter R within worst case equivalent heat production of said batteryΩ,maxThe average entropy of the battery is changed into a heat generation parameter UT,avgThe worst entropy change heat generation parameter U of the batteryT,maxIs little affected by the noise of the signal samples. And after the internal short circuit fault occurs, the internal thermal parameter R is generated by the worst equivalent heat generation of the batteryΩ,maxWorst entropy change heat production parameter U of sum cellT,maxObviously deviates from the average equivalent heat production internal thermal parameter R of the batteryΩ,avgAnd the average entropy of the battery is changed into a heat generation parameter UT,avgAnd the method is used for judging the abnormal heat generation fault caused by the internal short circuit more reliably.
Referring to FIG. 5, in one embodiment, the heat production level can be divided into 1-5 levels by abnormal heat production factors. Y isT<2 is 0 grade (no abnormality), and 2 is less than or equal to YT<2.5 is grade 1 abnormality, Y is not less than 2.5T<3 is 2 grade, and Y is more than or equal to 3T<3.5 is grade 3, and Y is more than or equal to 3.5T<4 is 4-stage, YT>4 is 5 grades. After 3598s triggers the short circuit in the battery, the abnormal factor obviously rises rapidly and has monotonous trend, and the abnormal factor can be used for judging the abnormal heat generation state of the battery. In one of the embodiments, Y can be considered to beT>At 2.5 (time 4000s), the abnormal heat generation of the battery is already obvious (more than 150% larger than the normal heat generation), and the abnormal heat generation and the suspected internal short circuit state are determined. Referring to fig. 3, the difference between the maximum temperature and the average temperature of the battery is only 6 ℃, and it is impossible to determine that the battery is abnormal by using the conventional method.
In an embodiment of the present invention, the battery heat generation abnormality (Y) determined by the above-described real-time battery internal short detection methodT>2.5) occurred at 4000s (402 s for cumulative use). In fact, using the same test conditions, the time from the triggering of an internal short circuit to the occurrence of a severe thermal runaway for this type of battery is approximatelyWas 2963 s. The heat generation abnormality detected by the real-time battery internal short-circuit detection method was advanced 2561s (42mi n41s) with respect to the final occurrence of thermal runaway. Therefore, the real-time battery internal short circuit detection method can accurately predict the battery internal short circuit condition.
Referring to fig. 6, an embodiment of the invention further provides a real-time battery internal short circuit detection apparatus 10. The real-time in-battery short circuit detection apparatus 10 includes a real-time in-battery short circuit detection device 11 and a computer 12. Wherein the computer 12 comprises a memory 100, a processor 200 and a computer program 300 stored on the memory 100 and executable on the processor 200. The processor 200 performs the real-time intra-cell short detection method, the method comprising:
s10, acquiring battery temperature data and battery current data of the battery;
s20, calculating a battery standard value according to the battery temperature data and the battery current data;
s30, according to the battery standard value, according to the battery thermal parameter identification standard formula, obtaining a thermal parameter comparison value;
and S40, judging whether the battery abnormally generates heat according to the thermal parameter comparison value.
The embodiment of the invention also provides a computer readable storage medium. The computer readable storage medium has stored thereon a computer program. The program is executable by a processor for the steps of the real-time battery short detection method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to a computer program or instructions, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (8)

1. A real-time battery internal short circuit detection method is characterized by comprising the following steps:
s10, acquiring battery temperature data and battery current data of the battery;
s20, calculating battery standard value including battery average temperature value T according to the battery temperature data and the battery current dataavgAnd the maximum temperature value T of the batterymax；
S310, acquiring battery experiment temperature data and battery experiment current data;
s320, calculating a battery thermal parameter discrimination standard formula according to the battery temperature experimental data and the battery experimental current data of the battery and a battery thermal model, wherein the battery thermal model is as follows:
Figure FDA0002182079990000011
wherein M is the mass of the battery and the unit is kg; cpThe specific heat capacity of the battery is J.kg-1·K-1；Is the derivative of the battery temperature T with respect to time, and has the unit ℃ · s-1(ii) a h is the average heat transfer coefficient of the battery to the environment and has the unit of W.m2·K-1(ii) a A is the average heat dissipation area of the battery, and the unit is m2(ii) a T is a battery temperature value and the unit is; t is∞Is ambient temperature in units of; i is the battery current value, and the unit is A; rΩRepresenting the equivalent heat production internal resistance parameter of the battery, and the unit is omega; t isKThe temperature of the battery is converted into temperature in Kelvin and is expressed in K and TK＝T+273.15；UTRepresenting the parameter of heat production caused by entropy change of the battery and having a unit of V.K-1；
Step S330, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the equivalent heat generation internal resistance parameter R of the batteryΩRespectively calculating the average equivalent heat production internal thermal resistance parameter R of the batteryΩ,avgAnd a resistance to heat parameter R within worst case equivalent heat production of said batteryΩ,max；
Step S340, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the battery entropy change heat production parameter UTRespectively calculating the average entropy variation heat production parameter U of the batteryT,avgThe worst entropy change heat generation parameter U of the batteryT,max；
S410, obtaining abnormal heat production factors
Figure FDA0002182079990000013
S420, mixing the normal heat production factor YTWith a predetermined threshold value ΛTThe comparison determines whether the battery pack abnormally generates heat.
2. The real-time intra-cell short circuit detection method of claim 1, wherein the step S20 includes:
s210, averaging according to the battery temperature data to obtain the average temperature value T of the batteryavg；
S220, selecting the maximum value in the battery temperature data as the maximum temperature value T of the batterymax。
3. The real-time intra-cell short circuit detection method of claim 1, further comprising, before the step S320:
and S311, based on the battery heat generation model, carrying out noise reduction processing on the battery temperature experimental data and the battery experimental current data.
4. The method of real-time intra-cell short detection as claimed in claim 3, wherein said method of noise reduction processing comprises a recursive filtering with a forgetting factor.
5. The real-time battery internal short circuit detection method according to claim 1, wherein the battery temperature experiment data includes the battery temperature values T acquired at equal time intervals, and the battery experiment current data includes the battery current values I acquired at equal time intervals.
6. The real-time intra-cell short circuit detection method of claim 1, wherein the predetermined threshold Λ isTIncluding at least a first anomaly threshold value LambdaT1And a second anomaly threshold value ΛT2Said second anomaly threshold value ΛT2Is greater than the first anomaly threshold value lambdaT1The exception level of (2).
7. A real-time intra-battery short detection apparatus, comprising a real-time intra-battery short detection device (11) and a computer (12), wherein the computer (12) comprises a memory (100), a processor (200) and a computer program (300) stored on the memory (200) and executable on the processor (200), characterized in that the processor (200) executes the computer program (300) by using a real-time intra-battery short detection method, the method comprising:
s10, acquiring battery temperature data and battery current data of the battery;
s20, calculating battery standard value including battery average temperature value T according to the battery temperature data and the battery current dataavgAnd the maximum temperature value T of the batterymax；
S310, acquiring battery experiment temperature data and battery experiment current data;
s320, calculating a battery thermal parameter discrimination standard formula according to the battery temperature experimental data and the battery experimental current data of the battery and a battery thermal model, wherein the battery thermal model is as follows:
wherein M is the mass of the battery and the unit is kg; cpThe specific heat capacity of the battery is J.kg-1·K-1；Is the derivative of the battery temperature T with respect to time, and has the unit ℃ · s-1(ii) a h is the average heat transfer coefficient of the battery to the environment and has the unit of W.m2·K-1(ii) a A is the average heat dissipation area of the battery, and the unit is m2(ii) a T is a battery temperature value and the unit is; t is∞Is ambient temperature in units of; i is the battery current value, and the unit is A; rΩRepresenting the equivalent heat production internal resistance parameter of the battery, and the unit is omega; t isKThe temperature of the battery is converted into temperature in Kelvin and is expressed in K and TK＝T+273.15；UTRepresenting the parameter of heat production caused by entropy change of the battery and having a unit of V.K-1；
Step S330, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the equivalent heat generation internal resistance parameter R of the batteryΩRespectively calculating the average equivalent heat production internal thermal resistance parameter R of the batteryΩ,avgAnd a resistance to heat parameter R within worst case equivalent heat production of said batteryΩ,max；
Step S340, according to the average temperature value T of the batteryavgAnd said battery maximum temperature value TmaxAccording to the entropy of the batteryVariable heat production parameter UTRespectively calculating the average entropy variation heat production parameter U of the batteryT,avgThe worst entropy change heat generation parameter U of the batteryT,max；
S410 obtaining abnormal heat production factor
Figure FDA0002182079990000031
S420 combining the normal heat production factor YTWith a predetermined threshold value ΛTThe comparison determines whether the battery pack abnormally generates heat.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 6.
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