A lithium battery state of health estimation method and system based on internet of things

Abstract

The application provides a lithium battery health state estimation method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the following steps: acquiring connection data of a target electric bicycle when the electric bicycle is charged in an electric cabinet, and transmitting the connection data to the cloud; obtaining an initial predicted lithium battery health score of the target electric bicycle from the cloud based on a target initial battery capacity and a target electric bicycle power change prediction of the target electric bicycle after the target electric bicycle is charged to a rated power of the electric cabinet; obtaining target parameter values of a plurality of target parameters related to prediction deviation correction, and transmitting the target parameter values to the cloud; obtaining a target lithium battery health score of the target electric bicycle after the initial lithium battery health score is corrected for deviation based on the target parameter values by the cloud; obtaining a charging scheme of the target electric bicycle generated by the cloud based on the target lithium battery health score, and sending the charging scheme to the electric cabinet, so that an accurate charging scheme is provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a lithium battery state of health estimation method and system based on Internet of Things. BACKGROUND

[0002] With the wide popularity and large-scale operation of electric bicycles in the world, people have higher and higher requirements for electric bicycles, and pay more and more attention to the state of health (SOH) of lithium batteries, which are the core power source of electric bicycles. The performance of lithium batteries will inevitably age with the increase of charge and discharge cycles, the passage of time and improper use, which is manifested as attenuation of available capacity and increase of internal resistance. Electric bicycle manufacturers will accurately calculate the SOH of electric bicycles when they leave the factory or when the vehicles return to the factory for maintenance. This detection method has a long time interval and cannot obtain the recent SOH of lithium batteries for accurate charging according to the SOH. When the electric cabinet charges the electric bicycle, it generally provides a fixed charging scheme (fixed voltage, fixed time). The fixed charging scheme often causes the lithium battery to be in an undercharged state or an overcharged state. The fixed charging scheme cannot provide an accurate charging scheme according to the SOH of the lithium battery. How to design an accurate charging scheme has been explored. SUMMARY

[0003] In view of the above technical problems, the technical scheme adopted by the present application is as follows:

[0004] According to a first aspect of the present application, a lithium battery state of health estimation method based on Internet of Things is provided. The method is used to calculate the lithium battery health score of a target electric bicycle to provide a charging scheme. The method comprises the following steps:

[0005] Obtaining connection data of the target electric bicycle when it is charged in the electric cabinet, and transmitting the connection data to the cloud. The connection data at least includes: the model of the target electric bicycle, the change of the proportion of the electric quantity of the target electric bicycle after the electric cabinet charges the target electric bicycle with a rated electric quantity;

[0006] Obtaining the initial predicted lithium battery health score of the target electric bicycle predicted by the cloud, wherein the cloud stores the target initial battery capacity corresponding to the model of the target electric bicycle; the initial predicted lithium battery health score is determined based on the target initial battery capacity and the change of the proportion of the electric quantity of the target electric bicycle after the electric cabinet charges the target electric bicycle with a rated electric quantity;

[0007] Obtaining target parameter values of a plurality of target parameters related to prediction deviation correction, and transmitting the target parameter values to the cloud; the target parameters at least include: an electric cabinet error parameter related to prediction deviation correction;

[0008] obtain the target lithium battery health score of the target electric bicycle from the cloud based on the target lithium battery health score, and send the charging scheme of the target electric bicycle to the electric cabinet, wherein the charging scheme at least comprises a charging power and a charging time length.

[0009] obtain the target lithium battery health score of the target electric bicycle from the cloud based on the target lithium battery health score, and send the charging scheme of the target electric bicycle to the electric cabinet, wherein the charging scheme at least comprises a charging power and a charging time length.

[0010] According to the second aspect of the present application, a lithium battery health state estimation system based on the Internet of Things is provided, comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the computer program is loaded and executed by the processor to realize the method described above.

[0011] The present application has at least the following beneficial effects: in summary, the connection data of the target electric bicycle during charging in the electric cabinet is obtained and transmitted to the cloud, the initial predicted lithium battery health score of the target electric bicycle is obtained from the cloud based on the target initial battery capacity and the change in the target electric bicycle's electric quantity after the target electric bicycle is charged by the rated electric quantity, the target parameter values of a plurality of target parameters related to the prediction deviation correction are obtained and transmitted to the cloud, the target lithium battery health score after the initial lithium battery health score is corrected by the cloud based on the target parameter values is obtained, the charging scheme of the target electric bicycle generated by the cloud based on the target lithium battery health score is obtained and sent to the electric cabinet, the lithium battery health score is roughly calculated by the change in the electric quantity of the rated electric quantity charging, and the lithium battery health score is corrected by the target parameter, and the charging scheme is determined according to the corrected lithium battery health score, so that an accurate charging scheme is provided, and the lithium battery is prevented from being in an undercharged state or an overcharged state by the fixed charging scheme. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0013] Figure 1 A flowchart of a lithium battery health state estimation method based on the Internet of Things is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0014] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0015] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0016] The embodiments of the present application provide a lithium battery health state estimation method based on Internet of Things, as shown in the figure, the method is used for calculating the lithium battery health score of the target electric bicycle to provide a charging scheme, the method comprises the following steps: Figure 1

[0017] S100, connection data of the target electric bicycle when charging in the electric cabinet is acquired, and the connection data is transmitted to the cloud, the connection data at least includes: the model of the target electric bicycle, the change of the proportion of the electric quantity of the target electric bicycle after the electric cabinet charges the target electric bicycle with rated electric quantity. Specifically, the change of the proportion of the electric quantity of the target electric bicycle after the electric cabinet charges the target electric bicycle with rated electric quantity, for example, the proportion of the electric quantity of the target electric bicycle is increased from 10% to 20% after the electric cabinet charges the target electric bicycle with rated electric quantity, then the change of the proportion of the electric quantity is 10%.

[0018] S200, the initial prediction lithium battery health score of the target electric bicycle predicted by the cloud is acquired, wherein the cloud stores the target initial battery capacity corresponding to the model of the target electric bicycle. The initial prediction lithium battery health score is determined based on the target initial battery capacity and the change of the proportion of the electric quantity of the target electric bicycle after the electric cabinet charges the target electric bicycle with rated electric quantity.

[0019] Specifically, based on the model of the target electric bicycle, the target initial battery capacity corresponding to the model of the target electric vehicle stored in the cloud is acquired; specifically, the rated electric quantity is E, the initial prediction lithium battery health score SOH0=C max ​Co, C max The current maximum capacity of the lithium battery for the electric bicycle, Co is the target initial battery capacity, the change a of the target electric bicycle after the electric cabinet charges the target electric bicycle with the rated electric quantity, then C max =E / a. SOH0=E / (a×Co).

[0020] S300, obtain target parameter values of a plurality of target parameters related to prediction deviation correction, and transmit the target parameter values to the cloud; the target parameters at least include: electric cabinet error parameters related to prediction deviation correction.

[0021] S400, obtain a target lithium battery health score after the initial lithium battery health score is corrected by the cloud based on the target parameter values.

[0022] S500, obtain a charging scheme of the target electric bicycle generated by the cloud based on the target lithium battery health score, and send the charging scheme to the electric cabinet; the charging scheme at least includes: charging power and charging time. In an embodiment of the present application, the charging scheme is determined based on the classification of the target lithium battery health score, for example, a high SOH uses a conventional charging power and a conventional charging time scheme, and a low SOH uses a low charging power and a long charging time.

[0023] In summary, the connection data of the target electric bicycle when charging in the electric cabinet is obtained, and the connection data is transmitted to the cloud; the initial prediction lithium battery health score of the target electric bicycle is obtained based on the target initial battery capacity and the change in the electric quantity of the target electric bicycle after the electric cabinet charges the target electric bicycle with the rated electric quantity; target parameter values of a plurality of target parameters related to prediction deviation correction are obtained, and the target parameter values are transmitted to the cloud; the target lithium battery health score after the initial lithium battery health score is corrected by the cloud based on the target parameter values is obtained; the charging scheme of the target electric bicycle generated by the cloud based on the target lithium battery health score is obtained, and the charging scheme is sent to the electric cabinet. The present application roughly calculates the lithium battery health score by the change in the electric quantity of the charging with the rated electric quantity, and then corrects the lithium battery health score by the target parameters, determines the charging scheme according to the corrected lithium battery health score, provides an accurate charging scheme, and avoids the fixed charging scheme from making the lithium battery in an undercharged state or an overcharged state.

[0024] Specifically, the target parameters further include: non-electric cabinet parameters related to prediction deviation correction, the non-electric cabinet parameters at least include: temperature parameters.

[0025] Further, when the target parameter is a temperature parameter, the target parameter value of the target parameter is determined by the following steps:

[0026] S301, acquire a historical temperature rise data list, the historical temperature rise data list comprising a plurality of historical temperature data, the historical temperature data comprising: a lithium battery capacity of an electric bicycle, and temperature rise data when the electric bicycle is charged; the temperature rise data being a difference between a charging temperature and an ambient temperature when the electric bicycle with the lithium battery capacity is charged.

[0027] S302, construct a functional relationship between the lithium battery capacity of the electric bicycle and the temperature rise data based on the historical temperature data. Specifically, any method for constructing a functional relationship based on historical data in the prior art belongs to the protection scope of the present application, and will not be described here. In an embodiment of the present application, an exponential function relationship between the lithium battery capacity of the electric bicycle and the temperature rise data is constructed based on the historical temperature data.

[0028] S303, determine target temperature rise data based on the lithium battery capacity of the target electric bicycle and the functional relationship. Specifically, input the lithium battery capacity of the target electric bicycle into the functional relationship to obtain the target temperature rise data.

[0029] S304, determine the target parameter value corresponding to the target parameter when the target parameter is a temperature parameter based on the ambient temperature of the target electric bicycle and the target temperature rise data.

[0030] In summary, by acquiring a historical temperature rise data list, constructing a functional relationship between the lithium battery capacity of the electric bicycle and the temperature rise data based on the historical temperature data, determining target temperature rise data based on the lithium battery capacity of the target electric bicycle and the functional relationship, and determining the target parameter value corresponding to the target parameter when the target parameter is a temperature parameter based on the ambient temperature of the target electric bicycle and the target temperature rise data, the influence of the lithium battery capacity on the temperature is introduced, so that the target parameter value of the target parameter is more accurately determined.

[0031] Specifically, S300 acquires the target parameter by the following steps:

[0032] S310, acquire a historical data list, the historical data list comprising n pieces of historical data of historical electric bicycles collected during historical charging, the historical data comprising: a parameter value of each preset initial parameter related to prediction deviation correction of the historical electric bicycles collected during historical charging, and an error value of the historical electric bicycles collected during historical charging; the error value being a difference between a historical predicted lithium battery health score of the historical electric bicycle and an actual lithium battery health score of the historical electric bicycle; wherein the cloud determines the historical predicted lithium battery health score of the historical electric bicycle based on a historical initial battery capacity of the historical electric vehicle and a change in the proportion of the electric quantity of the target electric bicycle after the rated electric quantity of the historical electric bicycle is charged.

[0033] S320, based on the historical data list, obtaining the correlation degree of each preset initial parameter and error value, if the absolute value of the correlation degree of a preset initial parameter and error value is greater than a preset significant threshold, the preset initial parameter is taken as the target parameter.

[0034] In an embodiment of the present application, S320 obtains the correlation degree of the preset initial parameter and the error value, specifically comprising:

[0035] S321, obtaining the average value of the parameter value of the preset initial parameter in the plurality of historical data as the initial parameter average value.

[0036] S322, obtaining the average value of the error value in the plurality of historical data as the intermediate mean value.

[0037] S323, obtaining the correlation degree of the preset initial parameter and the error value, the correlation degree satisfies the following conditions: the correlation degree is equal to the product value of the square root value of the sum value of n first product values and the square root value of the sum value of n third product values; the first product value is the product value of the first initial difference value and the second initial difference value, the second product value is the square value of the first initial difference value, the first initial difference value is the difference value between the parameter value of the preset initial parameter and the initial parameter average value, and the second initial difference value is the difference value between the error value of the historical electric bicycle and the intermediate mean value. It can be understood that the Pearson coefficient is used to determine the correlation between the preset initial parameter and the error value.

[0038] In another embodiment of the present application, S320 obtains the correlation degree of the preset initial parameter and the error value, specifically comprising:

[0039] S3201, taking the quotient value of the error value in the historical data and the first mean value as the first intermediate value, thereby obtaining a first intermediate value list, the first intermediate value list comprising n first intermediate values; the first mean value is the mean value of all error values.

[0040] S3202, for a preset initial parameter, obtaining a second intermediate value list of the preset initial parameter, the second intermediate value list comprising a plurality of second intermediate values, the second intermediate value being the quotient value of the parameter value of the preset initial parameter and the second mean value corresponding to the preset initial parameter, and the second mean value corresponding to the preset initial parameter being the mean value of the parameter value of the preset initial parameter in n historical data.

[0041] S3203, for a preset initial parameter, based on the second intermediate value list of the preset initial parameter, obtaining a difference value list of the preset initial parameter, the difference value list comprising a plurality of difference values, the difference value being the absolute value of the difference between the second intermediate value and the first intermediate value.

[0042] S3204, obtain the maximum difference in all difference lists as the maximum difference, and obtain the minimum difference in all difference lists as the minimum difference.

[0043] S3205, obtain the correlation degree of the preset initial parameter and the error value, the correlation degree of the preset initial parameter being an average value of initial degree values corresponding to parameter values of all preset initial parameters, the initial degree value corresponding to the parameter value of the preset initial parameter being equal to a quotient value of a first sum value and a second sum value, the first sum value being a sum value of the minimum difference and the maximum difference multiplied by a preset multiple, and the second sum value being a sum value of the parameter value of the preset initial parameter and the maximum difference multiplied by the preset multiple. The preset multiple is in a range of 0 to 1, and in an embodiment of the present application, the preset multiple is 0.5. It can be understood that S3201-S3205 are a complementary scheme of S321-S323, and in an embodiment of the present application, the correlation degree calculated by the method of S3201-S3205 and the correlation degree calculated by the method of S321-S323 are comprehensively determined as the final correlation degree.

[0044] Further, obtaining the target lithium battery health score of the initial lithium battery health score corrected by the target parameter value based on the cloud further comprises:

[0045] S001, obtaining a weighted sum value of all target parameter values calculated by the cloud as the specified error value.

[0046] Specifically, the weight of the target parameter value is determined based on the correlation degree of the preset initial parameter and the error value.

[0047] S002, obtaining the target lithium battery health score calculated by the cloud, the target lithium battery health score satisfying the following condition: the target lithium battery health score is equal to a sum value of the specified error value and the initial lithium battery health score.

[0048] In an embodiment of the present application, when the target parameter only includes the cabinet error parameter related to the prediction bias correction, and does not include the error parameter of the non-cabinet, the ratio of the correlation degree of the cabinet error parameter to the error value and the correlation degree of the non-cabinet error parameter to the error value is determined as the target ratio based on the training data; after the specified error value is determined, the target error value is determined based on the target ratio, the ratio of the specified error value and the target error value being equal to the target ratio; and the target lithium battery health score is equal to a sum value of the specified error value, the target error value and the initial lithium battery health score.

[0049] Embodiments of the present application also provide a lithium battery health state estimation system based on the Internet of Things, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the computer program being loaded and executed by the processor to implement the method provided by the above-mentioned embodiments.

[0050] Embodiments of the present invention also provide an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method provided in the above embodiments.

[0051] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0052] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A method for estimating the health status of a lithium battery based on the Internet of Things, characterized in that, The method is used to calculate the lithium battery health score of a target electric bicycle to provide a charging solution, and the method includes the following steps: The connection data of the target electric bicycle when it is being charged in the charging cabinet is obtained and transmitted to the cloud. The connection data includes at least the model of the target electric bicycle and the change in the percentage of the target electric bicycle’s battery capacity after the charging cabinet charges the target electric bicycle to its rated capacity. The initial predicted lithium battery health score of the target electric bicycle is obtained from the cloud. The cloud stores the target initial battery capacity corresponding to the model of the target electric bicycle. The initial predicted lithium battery health score is determined based on the target initial battery capacity and the change in the battery percentage of the target electric bicycle after the charging cabinet charges the target electric bicycle with the rated power. Obtain target parameter values ​​for several target parameters related to prediction deviation correction, and transmit the target parameter values ​​to the cloud; the target parameters include at least: cabinet error parameters related to prediction deviation correction; the target parameters are obtained through the following steps: A historical data list is obtained, which includes historical data of n historical electric bicycles collected during historical charging. The historical data includes: parameter values ​​of preset initial parameters related to prediction deviation correction for each historical electric bicycle collected during historical charging, and error values ​​of the historical electric bicycles collected during historical charging. The error value is the difference between the historical predicted lithium battery health score and the actual lithium battery health score of the historical electric bicycle. The cloud determines the historical predicted lithium battery health score of the historical electric bicycle based on the historical initial battery capacity of the historical electric bicycle and the change in the battery ratio of the target electric bicycle after charging the historical electric bicycle with the rated power. Based on a list of historical data, the correlation between each preset initial parameter and the error value is obtained. If the absolute value of the correlation between a preset initial parameter and the error value is greater than a preset significance threshold, the preset initial parameter is used as the target parameter. Specifically, obtaining the correlation between the preset initial parameter and the error value includes: Obtain the average value of the preset initial parameters from several historical data sets, and use it as the average value of the initial parameters; The average of the error values ​​from several historical data points is used as the median mean. The correlation between preset initial parameters and error values ​​is obtained, and the correlation satisfies the following conditions: the correlation is equal to the quotient of the first value and the second value, the first value is the sum of n first product values, the second value is the product of the square root of the sum of n second product values ​​and the square root of the sum of n third product values; the first product is the product of the first initial difference and the second initial difference, the second product is the square of the first initial difference, the first initial difference is the difference between the parameter value of the preset initial parameters and the average value of the initial parameters, and the second initial difference is the difference between the error value of historical electric bicycles and the median average value; Specifically, determining the correlation between the initial parameter and the error value includes: The quotient of the error value in the historical data and the first mean is used as the first intermediate value, thereby obtaining a list of first intermediate values, which includes n first intermediate values; the first mean is the average of all error values. For a preset initial parameter, obtain a second intermediate value list of the preset initial parameter. The second intermediate value list includes several second intermediate values. The second intermediate value is the quotient of the parameter value of the preset initial parameter and the second mean value corresponding to the preset initial parameter. The second mean value corresponding to the preset initial parameter is the mean value of the parameter value of the preset initial parameter in n historical data. For a preset initial parameter, based on a second intermediate value list of the preset initial parameter, a difference list of the preset initial parameter is obtained. The difference list includes several differences, and the difference is the absolute value of the difference between the second intermediate value and the first intermediate value. Get the largest difference from the list of all differences as the maximum difference, and get the smallest difference from the list of all differences as the minimum difference; Obtain the correlation between the preset initial parameters and the error value. The correlation between the preset initial parameters is the average of the initial degree values ​​corresponding to the parameter values ​​of all preset initial parameters. The initial degree value corresponding to the parameter value of the preset initial parameters is equal to the quotient of the first sum and the second sum. The first sum is the sum of the minimum difference and the maximum difference of the preset multiple. The second sum is the sum of the maximum difference between the parameter value of the preset initial parameters and the preset multiple. The preset multiple ranges from 0 to 1. Obtain the target lithium battery health score after the cloud performs deviation correction on the initial lithium battery health score based on the target parameter values; wherein, obtaining the target lithium battery health score after the cloud performs deviation correction on the initial lithium battery health score based on the target parameter values ​​also includes: Obtain the weighted sum of all target parameter values ​​calculated in the cloud as the specified error value; Obtain the target lithium battery health score calculated in the cloud. The target lithium battery health score must meet the following condition: the target lithium battery health score is equal to the sum of the specified error value and the initial lithium battery health score. Obtain the charging plan for the target electric bicycle generated by the cloud based on the target lithium battery health score, and send the charging plan to the charging cabinet. The charging plan includes at least: charging power and charging time.

2. The method for estimating the health status of a lithium battery based on the Internet of Things according to claim 1, characterized in that, The target parameters also include: non-electrical cabinet parameters related to prediction deviation correction, wherein the non-electrical cabinet parameters include at least: temperature parameters.

3. The method for estimating the health status of lithium batteries based on the Internet of Things according to claim 2, characterized in that, When the target parameter is a temperature parameter, the target parameter value is determined by the following steps: Obtain a list of historical temperature rise data, which includes several historical temperature data points, including: the lithium battery capacity of the electric bicycle and the temperature rise data when the electric bicycle is being charged; the temperature rise data is the difference between the charging temperature and the ambient temperature when the electric bicycle with the lithium battery capacity is being charged. A functional relationship between the lithium battery capacity and temperature rise data of electric bicycles was constructed using historical temperature data. The target temperature rise data is determined based on the lithium battery capacity and functional relationship of the target electric bicycle. Based on the ambient temperature and target temperature rise data of the target electric bicycle, determine the target parameter value corresponding to the target parameter when the target parameter is a temperature parameter.

4. The method for estimating the health status of a lithium battery based on the Internet of Things according to claim 1, characterized in that, The weights of the target parameter values ​​are determined based on the correlation between the preset initial parameters and the error values.

5. The method for estimating the health status of a lithium battery based on the Internet of Things according to claim 1, characterized in that, The preset multiplier is 0.

5.

6. An Internet of Things-based lithium battery health status estimation system, comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the Internet of Things-based lithium battery health state estimation method as described in any one of claims 1-5.

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