Method, device, electronic device and storage medium for determining abnormality of lithium-ion battery

Abstract

The present application discloses a method, device, electronic device and storage medium for determining abnormalities in a lithium-ion battery, belonging to the technical field of lithium-ion batteries. Among them, the method for determining abnormalities in a lithium-ion battery includes: obtaining the concentration information of the gas generated by the lithium-ion battery; when the concentration information exceeds the abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal working condition. This method can use the threshold setting of the characteristic gas of the lithium-ion battery and the threshold of the characteristic gas change rate as the hierarchical warning parameters of the lithium-ion battery, so that the characteristics of an impending thermal runaway can be identified as early as possible, and it has higher warning accuracy compared with the surface temperature and pressure distribution of the battery.

Description

Technical Field

[0001] The present application belongs to the technical field of lithium-ion batteries, and specifically relates to a method, device, electronic device and storage medium for determining abnormality of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries have gradually replaced traditional batteries due to their high energy density, high average output voltage, low self-discharge, and no memory effect. They have played an important role in the portable 3C field, energy storage field, and special use scenarios. Similarly, lithium-ion batteries are also facing the need for consumption iteration and usage upgrades. The market has also put forward higher requirements for lithium-ion batteries, especially safety performance during use. The national standard "Safety Requirements for Electric Vehicles" clearly requires that electric vehicles should use signal devices to buy escape time for drivers and passengers in case of accidents.

[0003] The problems with the early warning technology currently used in lithium-ion power batteries are mainly reflected in two aspects: on the one hand, due to the single acquisition signal, including the commonly used current, voltage and bus tab temperature, the early warning mode is single and the early warning accuracy is relatively low; on the other hand, the monitoring objects are mostly at the module level or battery pack level, and the early warning response is relatively delayed, making it impossible to achieve early warning before thermal runaway of the battery. Summary of the invention

[0004] The purpose of the present application is to provide a lithium-ion battery abnormality determination method, device, electronic device and storage medium to perform abnormal detection on lithium-ion batteries, accurately and timely discover the abnormal operating state of lithium ions, and then issue a lithium-ion battery warning.

[0005] According to a first aspect of an embodiment of the present application, a method for determining abnormality of a lithium-ion battery is provided, and the method may include:

[0006] Obtain the concentration information of gas produced by lithium-ion batteries;

[0007] When the concentration information exceeds an abnormal concentration threshold, it is determined that the lithium-ion battery is in an abnormal operating condition.

[0008] In some optional embodiments of the present application, the obtaining of the concentration information of the gas produced by the lithium-ion battery includes at least one of the following:

[0009] Obtain oxygen concentration information of gas produced by lithium-ion batteries;

[0010] Obtain information on the carbon dioxide concentration of gas produced by lithium-ion batteries;

[0011] Obtain information on the concentration of volatile organic gases emitted by lithium-ion batteries.

[0012] In some optional embodiments of the present application, the abnormal concentration threshold is generated by an adaptive algorithm;

[0013] Before acquiring the concentration information of the gas generated by the lithium-ion battery, the lithium-ion battery abnormality determination method further includes determining a gas concentration determination parameter of an adaptive algorithm.

[0014] In some optional embodiments of the present application, the adaptive algorithm is an exponential smoothing moving average algorithm;

[0015] The step of determining the gas concentration determination parameters of the adaptive algorithm comprises:

[0016] Step 1: Obtain the concentration information of the gas produced by the lithium-ion battery, where the oxygen concentration is C 01 , the carbon dioxide concentration is C 02 , the concentration of volatile organic gas is C 03 , let C n1 =C 01 , C n2 =C 02 , C n3 =C 03 ;

[0017] Step 2: After k time intervals t, the oxygen concentration is C k1 , the carbon dioxide concentration is C k2 , the concentration of volatile organic gas is C k3 , if C k1 >C n1 ,C k2 >C n2 ,C k3 >C n3 , then let C m1 =C k1 , C m2 =C k2 , C m3 =C k3 , D 1 =C m1 -C n1 , D n2 =C m2 -C n2 , D n3 =C m3 -C n3 , update C according to the exponential moving average algorithm EMA(X,N) m1 , C m2 and C m3 , preferably N = 2, let C m1 =(2 / 3)C m1 +(1 / 3)C n1 , C m2 =(2 / 3)Cm2 +(1 / 3)C n2 , C m3 =(2 / 3)C m3 +(1 / 3)C n3 ;

[0018] Step 3: After k+1 time intervals t, the oxygen concentration is C (k+1)1 , carbon dioxide is C (k+1)2 , volatile organic gas is C (k+1)3 , if the oxygen concentration satisfies C m1 <C (k+1)1 <C m1 +D n1 , the carbon dioxide concentration satisfies C m2 <C (k+1)2 <C m2 +D n2 , the concentration of volatile organic gases meets C m3 <C (k+1)3 <C m3 +D n3 , then let C n1 =C m1 , C n2 =C m2 , C n3 =C m3 , and,C (k+1)1 , C (k+1)2 , C (k+1)3 The adaptive parameter C is completed by the step 2 algorithm m1 , C m2 , C m3 , D n1 , D n2 , D n3 The gas concentration determination parameters of the adaptive algorithm are obtained through iterative updates.

[0019] In some optional embodiments of the present application, when the concentration information exceeds the abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal operating condition is specifically:

[0020] When C (k+1)1 >C m1 +D n1 or C (k+1)2 >C m2 +D n2 or C (k+1)3 >C m3 +D n3 It is determined that the lithium-ion battery is in an abnormal operating condition.

[0021] In some optional embodiments of the present application, when the concentration information exceeds the abnormal concentration threshold, after determining that the lithium ion battery is in an abnormal operating condition, the lithium ion battery abnormality determination method further includes:

[0022] Issue abnormal operating condition reminders.

[0023] According to a second aspect of the embodiments of the present application, a lithium-ion battery detection method is provided. The method can use the lithium-ion battery abnormality determination method described in any one of the embodiments of the first aspect to perform abnormal operating condition detection of the lithium-ion battery.

[0024] According to a third aspect of an embodiment of the present application, a device for determining abnormality of a lithium-ion battery is provided, and the device may include:

[0025] An acquisition module is used to obtain the concentration information of the gas produced by the lithium-ion battery;

[0026] The abnormality determination module is used to determine that the lithium-ion battery is in an abnormal operating condition when the concentration information exceeds an abnormal concentration threshold.

[0027] According to a fourth aspect of an embodiment of the present application, an electronic device is provided, which may include:

[0028] processor;

[0029] a memory for storing processor-executable instructions;

[0030] The processor is configured to execute instructions to implement the lithium-ion battery abnormality determination method as shown in any one of the embodiments of the first aspect.

[0031] According to a fifth aspect of an embodiment of the present application, a storage medium is provided. When instructions in the storage medium are executed by a processor of an information processing device or a server, the information processing device or the server implements a lithium-ion battery abnormality determination method as shown in any one of the embodiments of the first aspect.

[0032] The above technical solution of the present application has the following beneficial technical effects:

[0033] The method of the embodiment of the present application obtains the concentration information of the gas produced by the lithium-ion battery; and analyzes the operating state of the lithium-ion battery according to the concentration information of the gas produced. When the concentration information exceeds the abnormal concentration threshold, the lithium-ion battery is determined to be in an abnormal operating condition. The method can use the threshold setting of the characteristic gas of the lithium-ion battery and the threshold of the characteristic gas change rate as the hierarchical warning parameters of the lithium-ion battery, so that the characteristics of the impending thermal runaway can be identified as early as possible, and it has higher warning accuracy than the surface temperature and pressure distribution of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of a method for determining abnormality of a lithium-ion battery in an exemplary embodiment of the present application;

[0035] Figure 2 This is a flow chart of a method for determining an abnormality of a lithium-ion battery in a specific embodiment of the present application;

[0036] Figure 3 is a distribution diagram of sensor installation positions in a lithium-ion battery in an exemplary embodiment of the present application;

[0037] Figure 4 is a schematic diagram of the structure of an electronic device in an exemplary embodiment of the present application;

[0038] Figure 5 It is a schematic diagram of the hardware structure of an electronic device in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with specific implementations and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present application. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concepts of the present application.

[0040] The accompanying drawings show schematic diagrams of layer structures according to embodiments of the present application. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clarity. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0041] Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0042] In the description of the present application, it should be noted that the terms "first", "second" and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0043] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0044] In conjunction with the accompanying drawings, the lithium-ion battery abnormality determination method, device, electronic device and storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0045] like Figure 1 As shown, in a first aspect of an embodiment of the present application, a method for determining abnormality of a lithium-ion battery is provided, and the method may include:

[0046] S110: Acquire concentration information of gas produced by the lithium-ion battery;

[0047] S120: When the concentration information exceeds an abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal operating condition.

[0048] The above-mentioned embodiment method can use the threshold setting of the characteristic gas of the lithium-ion battery and the threshold of the characteristic gas change rate as the lithium-ion battery hierarchical warning parameters, so that the impending thermal runaway characteristics can be identified as early as possible, and it has higher warning accuracy compared with the surface temperature and pressure distribution of the battery.

[0049] Compared with temperature, air pressure, smoke and other signals, gas signals can better reflect the intrinsic characteristics of electrochemical reactions and are a direct representation of the reaction products. In addition, gas monitoring at the cell level can detect abnormal battery operation conditions earlier and more accurately than gas monitoring at the module level and battery pack level.

[0050] For a clearer explanation, the above steps are described in detail below:

[0051] The above embodiment is based on the lithium-ion battery cell level, anchoring oxygen, carbon dioxide, and volatile organic gases for different intrinsic reaction mechanisms, and judging the actual condition of the battery through triple signal coupling, thereby improving the accuracy and timeliness of lithium-ion battery warning. Figure 2 As shown in the figure, the method can collect the gas production of lithium-ion batteries through sensors, and use adaptive functions to adaptively generate judgment parameters, and increase the weight of the current working condition according to the algorithm to reflect the current actual working condition of the battery. The warning parameter is related to the intrinsic characteristics of the electrochemical reaction and has high timeliness. It can accurately warn of thermal abuse, electrical abuse and their derived abnormal working conditions, thereby improving the safety of lithium-ion batteries.

[0052] In one embodiment, obtaining the concentration information of the gas produced by the lithium-ion battery includes at least one of the following:

[0053] Obtain oxygen concentration information of gas produced by lithium-ion batteries;

[0054] Obtain information on the carbon dioxide concentration of gas produced by lithium-ion batteries;

[0055] Obtain information on the concentration of volatile organic gases emitted by lithium-ion batteries.

[0056] The gas production of lithium-ion batteries is generally believed to be caused by the redox decomposition of the electrolyte on the electrode surface, usually mainly H 2 , CO 2 , CO, CH 4 , C 2 H 6 and C 2 H 4 This embodiment anchors effective characteristic gases among various gas types, thereby being able to accurately judge the operating state of the lithium-ion battery.

[0057] Research has found that the collapse of the positive electrode material structure (specifically layered materials, such as lithium-rich materials, ternary high-nickel materials, lithium cobalt oxide materials, etc.) is accompanied by a large amount of oxygen precipitation; when a single battery is overcharged, the carbon dioxide concentration will increase significantly compared to other gases; when the battery is charged and discharged under high temperature conditions, the decomposition of the organic electrolyte is accelerated, generating a large amount of volatile organic matter. By anchoring the three gases of oxygen, carbon dioxide, and volatile organic gases, most possible out-of-control conditions of lithium-ion batteries can be warned. The embodiment of the present application can use any one or a combination of oxygen, carbon dioxide, and volatile organic gases for detection and then warn of the battery condition, among which it is more reliable to warn of most possible out-of-control conditions of lithium-ion batteries by anchoring the three gases of oxygen, carbon dioxide, and volatile organic gases.

[0058] In one embodiment, the abnormal concentration threshold is generated by an adaptive algorithm;

[0059] Before obtaining the concentration information of the gas produced by the lithium-ion battery, the lithium-ion battery abnormality determination method also includes determining the gas concentration judgment parameters of the adaptive algorithm. The method can adaptively generate the judgment parameters. All parameters are continuously iterated and updated according to the judgment algorithm, and the actual working condition of the battery is reflected by increasing the weight of the current working condition.

[0060] The gas production of lithium-ion batteries is complex and changeable. The differences and similarities between positive electrode materials, negative electrode materials, and electrolytes, as well as changes in process conditions, will have a significant impact on the type and concentration of the final gas produced. In addition, the current operating conditions of lithium-ion batteries, such as heating and overcharging, will affect the gas production. There are great limitations in providing warnings based solely on thresholds obtained from previous experience. Selecting appropriate parameter thresholds to provide early thermal runaway warnings for single cells, thereby improving the safety of lithium-ion battery use and providing users with sufficient reaction and decision-making time, is still a direction that needs to be worked on for lithium-ion batteries to provide warnings through characteristic gases.

[0061] In one embodiment, the adaptive algorithm is an exponential moving average algorithm. Exemplarily, an exponential moving average algorithm (EMA) is selected. If Y=EMA(X, N), then Y=[2*X+(N-1)*Y'] / (N+1), where Y' represents the Y value of the previous period, and N=2 is optional.

[0062] The step of determining the gas concentration determination parameters of the adaptive algorithm comprises:

[0063] Step 1: Obtain the concentration information of the gas produced by the lithium-ion battery, where the oxygen concentration is C 01 , the carbon dioxide concentration is C 02 , the concentration of volatile organic gas is C 03 , let C n1 =C 01 , C n2 =C 02 , C n3 =C 03 ;

[0064] Step 2: After k time intervals t, the oxygen concentration is C k1 , the carbon dioxide concentration is C k2 , the concentration of volatile organic gas is C k3 , if C k1 >C n1 ,C k2 >C n2 ,C k3 >C n3 , then let C m1 =C k1 , C m2 =C k2 , C m3 =C k3 , D 1 =C m1 -C n1 , D n2 =C m2 -C n2 , D n3 =C m3 -C n3 , update C according to the exponential moving average algorithm EMA(X,2) m1 , C m2 and C m3 , let C m1 =(2 / 3)C m1 +(1 / 3)C n1 , C m2 =(2 / 3)C m2 +(1 / 3)C n2 , C m3 =(2 / 3)C m3+(1 / 3)C n3 ;

[0065] Step 3: After k+1 time intervals t, the oxygen concentration is C (k+1)1 , carbon dioxide is C (k+1)2 , volatile organic gas is C (k+1)3 , if the oxygen concentration satisfies C m1 <C (k+1)1 <C m1 +D n1 , the carbon dioxide concentration satisfies C m2 <C (k+1)2 <C m2 +D n2 , the concentration of volatile organic gases meets C m3 <C (k+1)3 <C m3 +D n3 , then let C n1 =C m1 , C n2 =C m2 , C n3 =C m3 , and C (k+1)1 , C (k+1)2 , C (k+1)3 The adaptive parameter C is completed by the step 2 algorithm m1 , C m2 , C m3 , D n1 , D n2 , D n3 The gas concentration determination parameters of the adaptive algorithm are obtained through iterative updates.

[0066] In one embodiment, when the concentration information exceeds an abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal operating condition is specifically:

[0067] When C (k+1)1 >C m1 +D n1 or C (k+1)2 >C m2 +D n2 or C (k+1)3 >C m3 +D n3 It is determined that the lithium-ion battery is in an abnormal operating condition.

[0068] The above-mentioned embodiment can use a gas sensor to monitor the gas production of lithium-ion batteries, by anchoring the three gases of oxygen, carbon dioxide, and volatile organic gases, wherein oxygen, carbon dioxide, and volatile organic gases are respectively targeted at different intrinsic reaction mechanisms, and a judgment method of adaptively generating judgment parameters is used to comprehensively warn of thermal abuse, electrical abuse, and abnormal operating conditions derived therefrom. The gas sensor can be a multi-component gas sensor or a single-component gas sensor to monitor the gas production of lithium-ion batteries in real time. The real-time signal of the gas sensor passes through a data acquisition module containing a filtering circuit, an amplifying circuit, and an analog-to-digital conversion circuit, and the data is calibrated and stored in the host computer. In addition, the host computer has a certain computing power for obtaining and updating adaptive parameters, and communicates with the BMS system through the CAN bus.

[0069] In one embodiment, after determining that the lithium ion battery is in an abnormal operating condition when the concentration information exceeds the abnormal concentration threshold, the lithium ion battery abnormality determination method further includes:

[0070] Issue abnormal operating condition reminders.

[0071] In a specific embodiment, three single-component sensors, namely, an oxygen sensor, a carbon dioxide sensor, and a volatile organic gas sensor, are selected and installed in the battery cell at position 3 shown in the figure, and a high-nickel ternary 811 / silicon-carbon single cell is taken as an example, and a constant current charge is performed at 0.5C current to 4.2V, and the gas production is sampled at a time interval of t=3s, wherein the first sampling data of the oxygen sensor, the carbon dioxide sensor, and the volatile organic gas sensor is C 01 , C 02 , C 03 , 3s later the data collected is C 11 , C 12 , C 13 , after k sampling cycles, the collected data is C k1 , C k2 , C k3 Then discharge to 2.5V with 0.5C constant current. After (k+i) sampling cycles, the data collected is C (k+i)1 , C (k+i)2 , C (k+i)3 , let the adaptive parameter C n1 , C n2 , C n3 C (k+i)1 , C (k+i)2 , C (k+i)3 The maximum value of

[0072] C n1 ={C (k+i)1}max;

[0073] C n2={C (k+i)2}max;

[0074] C n3 ={C (k+i)3}max;

[0075] In the specific implementation process, the acquisition module transmits the data to the host computer and saves it. The newly collected data after the time interval t is compared with the original saved data. If it is smaller than the original data, no operation is performed. If it is larger than the original data, it is updated to the newly collected data until all the data are traversed and the saved data is the maximum value.

[0076] After (k+i+j) sampling cycles, the collected data is C (k+i+j)1 , C (k+i+j)2 , C (k+i+j)3 , if C (k+i+j)1 >C n1 ,C (k+i+j)2 >C n2 ,C (k+i+j)3 >C n3 , let C m1 =C (k+i+j)1 , C m2 =C (k+i+j)2 , C m3 =C (k+i+j)3 , and get C m1 , C m2 , C m3 , let the adaptive parameter D n1 =C m1 -C n1 , D n2 =C m2 -C n2 , D n3 =C m3 -C n3 , C m1 , C m2 , C m3 Updated to C according to the EMA(X,2) algorithm m1 =(2 / 3)C m1 +(1 / 3)C n1 , C m2 =(2 / 3)C m2 +(1 / 3)C n2 , D m3 =(2 / 3)C m3 +(1 / 3)C n3 So far, all adaptive parameters are derived based on the current state of the single battery, and the current operating state is fully reflected by increasing the proportion of the most recent state.

[0077] In the subsequent single battery charge and discharge cycle process, the sampling data of the oxygen sensor, carbon dioxide sensor, and volatile organic gas sensor are C (n+1)1 , C (n+1)2 , C (n+1)3 If one or more sensor sampling values ​​appear C (n+1)1 >C m1 +D n1 , C (n+1)2 >C m2 +D n2 or C (n+1)3 >C m3 +D n3 , the host computer communicates with the BMS system through the CAN bus, and the BMS system issues a warning operation.

[0078] In addition, in this embodiment, based on the adaptive parameters, before the sensor is initialized, a fixed parameter C derived from experience may be added. △1 , C △2 , C △3 , if C △1 <C (n+1)1 <C m1 +D n1 , C △2 <C (n+1)2 <C m2 +D n2 , C △3 <C (n+1)3 <C m3 +D n3 If one or more of the above conditions are met, the host computer will communicate with the BMS system through the CAN bus, and the BMS system will issue a warning operation.

[0079] In another specific embodiment of the present application, based on the common warning methods in the industry including temperature, air pressure, smoke, etc., the technology of the above embodiment is expanded and extended to add a temperature sensor signal of the battery terminal ear located at the bus bar.

[0080] Similarly, taking the high nickel ternary 811 / silicon carbon single cell as an example, it is charged to 4.2V at a constant current of 0.5C, and the gas production is sampled at a time interval of t=5s. The first sampling data of the oxygen sensor, carbon dioxide sensor, volatile organic gas sensor, and temperature sensor is C 01 , C 02 , C 03 , C 04 , 5s later the data collected is C 11 , C 12 , C 13 , C 14 , after k sampling cycles, the data is C k1 , C k2, C k3 , C k4 Then discharge to 2.5V with 0.5C constant current. After (k+i) sampling cycles, the data collected is C (k+i)1 , C (k+i)2 , C (k+i)3 , C (k+i)4 , let the adaptive parameter C n1 , C n2 , C n3 , C n4 C (k+i)1 , C (k+i)2 , C (k+i)3 , C (k+i)4 The maximum value of

[0081] C n1 ={C (k+i)1}max;

[0082] C n2 ={C (k+i)2}max;

[0083] C n3 ={C (k+i)3}max;

[0084] C n4 ={C (k+i)4}max;

[0085] After (k+i+j) sampling cycles, the collected data is C (k+i+j)1 , C (k+i+j)2 , C (k+i+j)3 , C (k+i+j)4 , if C (k+i+j)1 >C n1 ,C (k+i+j)2 >C n2 ,C (k+i+j)3 >C n3 ,C (k+i+j)4 >C n4 , let C m1 =C (k+i+j)1 , C m2 =C (k+i+j)2 , C m3 =C (k+i+j)3 , C m4 =C (k+i+j)4 Get C m1 , C m2 , C m3 , C m4 , let the adaptive parameter D n1 =C m1 -C n1 , D n2 =C m2 -Cn2 , D n3 =C m3 -C n3 , D n4 =C m4 -C n4 , C m1 , C m2 , C m3 , C m4 Updated to C according to the EMA(X,2) algorithm m1 =(2 / 3)C m1 +(1 / 3)C n1 , C m2 =(2 / 3)C m2 +(1 / 3)C n2 , C m3 =(2 / 3)C m3 +(1 / 3)C n3 , C m4 =(2 / 3)C m4 +(1 / 3)C n4 .

[0086] In the subsequent single battery charge and discharge cycle process, the sampling data of the oxygen sensor, carbon dioxide sensor, volatile organic gas sensor and temperature sensor are C (n+1)1 , C (n+1)2 , C (n+1)3 , C (n+1)4 , the comparison operation results are all C (n+1)1 <C m1 +D n1 , C (n+1)2 <C m2 +D n2 , C (n+1)3 <C m3 +D n3 ,C (n+1)4 <C m4 +D n4 The warning will not be triggered if the oxygen sensor C (n+1)1 >C n1 , then the adaptive parameter C n1 , C m1 , D n1 Will be updated, let C n1 =C m1 , C m1 =C (n+1)1 , D n1 =C m1 -C n1 , then C m1 Will be updated to C again according to the algorithm EMA(X,2) algorithm m1 =(2 / 3)C m1+(1 / 3)C n1 , and the iterative update of the adaptive parameters is completed.

[0087] In the subsequent continuous collection process, the sampling data of the oxygen sensor, carbon dioxide sensor, volatile organic gas sensor and temperature sensor are C (n+2)1 , C (n+2)2 , C (n+2)3 , C (n+2)4 , if C (n+2)1 >C m1 +D n1 , C (n+1)2 <C m2 +D n2 , C (n+1)3 <C m3 +D n3 ,C (n+1)4 <C m4 +D n4 , then the host computer communicates with the BMS system through the CAN bus, and the BMS system issues a warning operation.

[0088] The above embodiments show that the present invention can fully serve as a single battery warning module to complement the warning functions at the module level and the battery pack level, and the warning determination method can also be extended to the module level and the battery pack level.

[0089] It should be noted that the method for determining lithium-ion battery abnormality provided in the embodiment of the present application can be executed by a lithium-ion battery abnormality determination device, or a control module in the lithium-ion battery abnormality determination device for executing the method for determining lithium-ion battery abnormality. In the embodiment of the present application, the method for determining lithium-ion battery abnormality performed by the lithium-ion battery abnormality determination device is taken as an example to illustrate the device for determining lithium-ion battery abnormality provided in the embodiment of the present application.

[0090] In a second aspect of the embodiments of the present application, a lithium-ion battery detection method is provided. The method can use the lithium-ion battery abnormality determination method described in any one of the embodiments of the first aspect to perform abnormal operating condition detection of the lithium-ion battery.

[0091] like Figure 3 As shown, in a third aspect of an embodiment of the present application, a device for determining abnormality of a lithium-ion battery is provided, and the device may include:

[0092] An acquisition module is used to obtain the concentration information of the gas produced by the lithium-ion battery;

[0093] The abnormality determination module is used to determine that the lithium-ion battery is in an abnormal operating condition when the concentration information exceeds an abnormal concentration threshold.

[0094] The above-mentioned embodiment device can use the threshold setting of the characteristic gas of the lithium-ion battery and the threshold of the characteristic gas change rate as the lithium-ion battery level warning parameters, so that the impending thermal runaway characteristics can be identified as early as possible, and it has higher warning accuracy compared to the surface temperature and pressure distribution of the battery.

[0095] The lithium-ion battery abnormality determination device in the embodiment of the present application can be a device, or a component, an integrated circuit, or a chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the embodiment of the present application is not specifically limited.

[0096] The lithium-ion battery abnormality determination device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0097] The lithium-ion battery abnormality determination device provided in the embodiment of the present application can achieve Figure 1 to Figure 2 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0098] Alternatively, if Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned lithium-ion battery abnormality determination method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0099] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0100] Figure 5 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of the present application.

[0101] The electronic device 500 includes but is not limited to: a radio frequency unit 501, a network module 502, an audio output unit 503, an input unit 504, a sensor 505, a display unit 506, a user input unit 507, an interface unit 508, a memory 509, and a processor 510.

[0102] Those skilled in the art will appreciate that the electronic device 500 may also include a power source (such as a battery) for supplying power to various components, and the power source may be logically connected to the processor 510 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 5 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0103] It should be understood that in the embodiment of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, and the graphics processor 5041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 507 includes a touch panel 5071 and other input devices 5072. The touch panel 5071 is also called a touch screen. The touch panel 5071 may include two parts: a touch detection device and a touch controller. Other input devices 5072 may include but are not limited to a physical keyboard, a function key (such as a volume control button, a switch button, etc.), a trackball, a mouse, and a joystick, which will not be repeated here. The memory 509 can be used to store software programs and various data, including but not limited to applications and operating systems. The processor 510 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, the user interface, and the application program, etc., and the modem processor mainly processes wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 510.

[0104] The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned lithium-ion battery abnormality determination method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0105] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0106] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned lithium-ion battery abnormality determination method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0107] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0108] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0110] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for determining abnormalities in a lithium-ion battery, characterized in that, it includes: Obtaining the concentration information of the gas generated by the lithium-ion battery; When the concentration information exceeds the abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal condition; The abnormal concentration threshold is generated by an adaptive algorithm; Determining the gas concentration determination parameters of the adaptive algorithm, including: Step 1: Obtain the concentration information of the gas generated by the lithium-ion battery, where the oxygen concentration is C 01 and the carbon dioxide concentration is C 02 and the concentration of volatile organic gases is C 03 . Let C n1 = C 01 , C n2 = C 02 , C n3 = C 03 ; Step 2: After k time intervals t, the oxygen concentration is C k1 , the carbon dioxide concentration is C k2 , and the volatile organic gas concentration is C k3 . If C k1 > C n1 , C k2 > C n2 , C k3 > C n3 , then let C m1 = C k1 , C m2 = C k2 , C m3 = C k3 , D 1 = C m1 - C n1 , D n2 = C m2 - C n2 , D n3 = C m3 - C n3 . Update C m1 , C m2 and C m3 according to the Exponential Moving Average algorithm EMA(X, N), and let C m1 = (2 / 3)C m1 + (1 / 3)C n1 , C m2 = (2 / 3)C m2 + (1 / 3)C n2 , C m3 = (2 / 3)C m3 + (1 / 3)C n3 ; Step 3: After k + 1 time intervals t, the oxygen concentration is C (k+1)1 , the carbon dioxide is C (k+1)2 , and the volatile organic gas is C (k+1)3 . If the oxygen concentration satisfies C m1 < C (k+1)1 < C m1 + D n1 , the carbon dioxide concentration satisfies C m2 < C (k+1)2 < C m2 + D n2 , and the volatile organic gas concentration satisfies C m3 < C (k+1)3 < C m3 + D n3 , then let C n1 = C m1 , C n2 = C m2 , C n3 = C m3 , and according to the data C (k+1)1 , C (k+1)2 , C (k+1)3 , complete the update iteration of the adaptive parameters C m1 , C m2 , C m3 , D n1 , D n2 , D n3 through the algorithm in Step 2 to obtain the gas concentration determination parameters of the adaptive algorithm.

2. The method for determining abnormalities in a lithium-ion battery according to claim 1, characterized in that, The obtaining the concentration information of the gas generated by the lithium-ion battery includes at least one of the following: Obtaining the oxygen concentration information of the gas generated by the lithium-ion battery; Obtaining the carbon dioxide concentration information of the gas generated by the lithium-ion battery; Obtaining the volatile organic gas concentration information of the gas generated by the lithium-ion battery.

3. The method for determining abnormalities in a lithium-ion battery according to claim 1, characterized in that, The when the concentration information exceeds the abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal condition is specifically: When C (k+1)1 > C m1 + D n1 or C (k+1)2 > C m2 + D n2 or C (k+1)3 > C m3 + D n3 it is determined that the lithium-ion battery is in an abnormal operating condition.

4. The method for determining abnormalities in a lithium-ion battery according to claim 1, characterized in that, After the when the concentration information exceeds the abnormal concentration threshold, determining that the lithium-ion battery is in an abnormal condition, the method for determining abnormalities in the lithium-ion battery further includes: Sending a reminder of an abnormal condition.

5. A method for detecting a lithium-ion battery, characterized in that, it includes: Using the method for determining abnormalities in a lithium-ion battery according to any one of claims 1-4 to detect abnormal conditions of the lithium-ion battery.

6. An apparatus for determining abnormalities in a lithium-ion battery, characterized in that, it includes: An acquisition module for acquiring the concentration information of the gas generated by the lithium-ion battery; An abnormality determination module for determining that the lithium-ion battery is in an abnormal condition when the concentration information exceeds the abnormal concentration threshold; The abnormal concentration threshold is generated by an adaptive algorithm; and is also used for determining the gas concentration determination parameters of the adaptive algorithm, including: Step 1: Obtain the concentration information of the gas generated by the lithium-ion battery, where the oxygen concentration is C 01 , the carbon dioxide concentration is C 02 , and the volatile organic gas concentration is C 03 . Let C n1 = C 01 , C n2 = C 02 , C n3 = C 03 ; Step 2: After k time intervals t, the oxygen concentration is C k1 , the carbon dioxide concentration is C k2 , and the volatile organic gas concentration is C k3 . If C k1 >C n1 , C k2 >C n2 , C k3 >C n3 , then let C m1 = C k1 , C m2 = C k2 , C m3 = C k3 , D 1 = C m1 - C n1 , D n2 = C m2 - C n2 , D n3 = C m3 - C n3 . Update C m1 , C m2 and C m3 according to the Exponential Moving Average algorithm EMA(X, N), and let C m1 = (2 / 3)C m1 + (1 / 3)C n1 , C m2 = (2 / 3)C m2 + (1 / 3)C n2 , C m3 = (2 / 3)C m3 + (1 / 3)C n3 ; Step 3: After k + 1 time intervals t, the oxygen concentration is C (k+1)1 , the carbon dioxide is C (k+1)2 , and the volatile organic gas is C (k+1)3 . If the oxygen concentration satisfies C m1 < C (k+1)1 < C m1 + D n1 , the carbon dioxide concentration satisfies C m2 < C (k+1)2 < C m2 + D n2 , and the volatile organic gas concentration satisfies C m3 < C (k+1)3 < C m3 + D n3 , then let C n1 = C m1 , C n2 = C m2 , C n3 = C m3 , and according to the data C (k+1)1 , C (k+1)2 , C (k+1)3 , complete the adaptive parameter C m1 , C m2 , C m3 , D n1 , D n2 , D n3 update iteration through the algorithm in Step 2 to obtain the gas concentration determination parameters of the adaptive algorithm.

7. An electronic device, characterized in that, it includes: A processor, a memory, and a program or instruction stored on the memory and executable on the processor, and when the program or instruction is executed by the processor, the steps of the method for determining abnormalities in a lithium-ion battery according to any one of claims 1-4 are implemented.

8. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method for determining abnormalities in a lithium-ion battery according to any one of claims 1-4 are implemented.

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