A method and system for predicting battery life based on vehicle battery health

Abstract

The present invention discloses a method and system for predicting battery life based on the health status of vehicle batteries, which relates to the technical field of commercial vehicle battery applications. The method includes obtaining mileage and SOH data during vehicle operation, and obtaining a mileage-SOH scatter plot based on the obtained mileage and SOH data; in the obtained mileage-SOH scatter plot, a preset number of points are selected according to a set rule as fitting reference points; based on the coordinates of the selected fitting reference points, fitting is performed to obtain an attenuation curve representing the relationship between mileage and SOH; according to the obtained attenuation curve, the mileage corresponding to when the SOH is a preset percentage value is calculated, and the calculated mileage is the warranty mileage when the SOH achievement rate is the preset percentage value. The present invention can predict battery life without using complex calculation methods and effectively evaluate the qualification rate of batteries during the warranty period.

Description

Technical Field

[0001] The present invention relates to the technical field of commercial vehicle battery applications, and specifically relates to a method and system for predicting battery life based on vehicle battery health. Background Art

[0002] At present, the number of electric vehicles in use is increasing year by year, and there are occasional problems in the market where the battery health fails to meet the standard during the warranty period, resulting in batch replacements. Automobile manufacturers generally convert the number of cycles into driving mileage, so as to promise mileage warranty to customers. When predicting the life of vehicles after they are successively put on the market, there are often many influencing factors, such as driving mileage, depth of discharge, discharge interval, storage conditions, energy recovery, etc. Therefore, it is necessary to further calculate using a neural network algorithm, and the calculation process is complex and inaccurate.

[0003] Currently, generally based on the cycle data of the battery at different temperatures and different SOC (State of Charge) and the storage data at different temperatures and different SOC, the actual operating data of the vehicle can be converted into storage data and cycle data to predict the battery service life. This method requires a large amount of basic data, and the differences between actual use and laboratory tests, such as the coupling effect of working conditions, sequence differences, discharge intervals, etc., are not considered when converting equivalent working conditions. Therefore, there is still a certain difference between the predicted accuracy and the actual results. It can be seen that the current battery life prediction methods are cumbersome and inaccurate. Summary of the Invention

[0004] Aiming at the defects existing in the prior art, the purpose of the present invention is to provide a method and system for predicting battery life based on vehicle battery health, which can predict the battery life without using complex calculation methods and effectively evaluate the qualification rate of the battery during the warranty period.

[0005] To achieve the above object, a method for predicting battery life based on vehicle battery health provided by the present invention specifically includes the following steps:

[0006] Obtain the mileage and SOH data during the operation of the vehicle, and obtain a mileage-SOH scatter distribution diagram based on the obtained mileage and SOH data;

[0007] In the obtained mileage-SOH scatter distribution diagram, select a preset number of points according to the set rules as the fitting reference points;

[0008] Based on the coordinates of the selected fitting reference points, perform fitting to obtain an attenuation curve representing the relationship between mileage and SOH;

[0009] According to the obtained attenuation curve, calculate the mileage corresponding to the preset percentage value of SOH, and the calculated mileage is the warranty mileage with the SOH achievement rate of the preset percentage value.

[0010] On the basis of the above technical solution, during the operation of the vehicle, the running data is sent to the big data monitoring platform of the vehicle enterprise through the T-box, and the running data includes the mileage and SOH data during the vehicle operation.

[0011] On the basis of the above technical solution, the big data monitoring platform of the vehicle enterprise makes a graph with the mileage as the abscissa and the SOH corresponding to the mileage as the ordinate according to the mileage and SOH data to obtain a mileage-SOH scatter distribution graph.

[0012] On the basis of the above technical solution,

[0013] Select a preset number of points according to the set rules as the fitting reference points. Specifically: after the starting mileage, select a preset number of points according to the set rules as the fitting reference points;

[0014] The starting mileage is the mileage that the vehicle has accumulated when the battery activation period ends.

[0015] On the basis of the above technical solution, the set rules are specifically as follows:

[0016] On the horizontal axis of the mileage-SOH scatter distribution graph, select a preset number of points at a fixed interval after the starting mileage as the reference reference points;

[0017] Based on the mileage-SOH scatter distribution graph, select a set number of points near each reference reference point and obtain the SOH value of each point;

[0018] For each reference reference point, sort the SOH values of the set number of points near the current reference reference point from high to low, and determine the points with the top preset percentage as the fitting reference points corresponding to the current reference reference point.

[0019] A system for predicting battery life based on the health of a vehicle battery provided by the present invention includes:

[0020] An acquisition module, which is used to acquire the mileage and SOH data during the operation of the vehicle, and obtain a mileage-SOH scatter distribution graph based on the acquired mileage and SOH data;

[0021] A selection module, which is used to select a preset number of points as fitting reference points according to the set rules in the obtained mileage-SOH scatter distribution graph;

[0022] A fitting module, which is used to perform fitting based on the coordinates of the selected fitting reference points to obtain an attenuation curve representing the relationship between mileage and SOH;

[0023] A calculation module, which is used to calculate the mileage corresponding to the SOH when it reaches a preset percentage value according to the obtained attenuation curve, and the calculated mileage is the warranty mileage when the SOH achievement rate reaches the preset percentage value.

[0024] On the basis of the above technical solution, during the operation of the vehicle, operation data is sent to the vehicle enterprise big data monitoring platform through the T-box, and the operation data includes the mileage and SOH data during the operation of the vehicle.

[0025] On the basis of the above technical solution, the vehicle enterprise big data monitoring platform makes a mileage-SOH scatter plot distribution map with the mileage as the abscissa and the SOH corresponding to the mileage as the ordinate according to the mileage and SOH data.

[0026] On the basis of the above technical solution,

[0027] Select a preset number of points as fitting reference points according to the set rules. Specifically: after the starting mileage, select a preset number of points as fitting reference points according to the set rules;

[0028] The starting mileage is the mileage that the vehicle has accumulated when the battery activation period ends.

[0029] On the basis of the above technical solution, the set rules are specifically:

[0030] On the horizontal axis of the mileage-SOH scatter plot distribution map, select a preset number of points at a fixed interval after the starting mileage as reference points;

[0031] Based on the mileage-SOH scatter plot distribution map, select a set number of points near each reference point and obtain the SOH value of each point;

[0032] For each reference point, sort the SOH values of the set number of points near the current reference point from high to low, and determine the points with the top preset percentage value as the fitting reference points corresponding to the current reference point.

[0033] Compared with the prior art, the advantages of the present invention are as follows: By obtaining the mileage and SOH data during the operation of the vehicle, and obtaining a mileage-SOH scatter plot based on the obtained mileage and SOH data, then in the obtained mileage-SOH scatter plot, a preset number of points are selected according to the set rules as fitting reference points, and then based on the coordinates of the selected fitting reference points, fitting is performed to obtain an attenuation curve representing the relationship between mileage and SOH. Then, according to the obtained attenuation curve, the mileage corresponding to the SOH being a preset percentage value is calculated, and the calculated mileage is the warranty mileage with the SOH achievement rate being the preset percentage value. By predicting the overall battery life of the vehicle through the existing health data of the actually used battery, the battery life can be predicted without using complex calculation methods, effectively evaluating the qualification rate of the battery during the warranty period, and providing necessary data support for the update of the subsequent software strategy of the vehicle enterprise. Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0035] Figure 1 It is a flowchart of a method for predicting battery life based on the health of a vehicle battery in an embodiment of the present invention.

[0036] Figure 2 It is a schematic diagram of the attenuation curve in an embodiment of the present invention. Detailed Embodiments

[0037] The embodiment of the present invention provides a method for predicting battery life based on the health of a vehicle battery. By obtaining the mileage and SOH data during the operation of the vehicle, and obtaining a mileage-SOH scatter plot based on the obtained mileage and SOH data, then in the obtained mileage-SOH scatter plot, a preset number of points are selected according to the set rules as fitting reference points, and then based on the coordinates of the selected fitting reference points, fitting is performed to obtain an attenuation curve representing the relationship between mileage and SOH. Then, according to the obtained attenuation curve, the mileage corresponding to the SOH being a preset percentage value is calculated, and the calculated mileage is the warranty mileage with the SOH achievement rate being the preset percentage value. By predicting the overall battery life of the vehicle through the existing health data of the actually used battery, the battery life can be predicted without using complex calculation methods, effectively evaluating the qualification rate of the battery during the warranty period, and providing necessary data support for the update of the subsequent software strategy of the vehicle enterprise. The embodiment of the present invention correspondingly also provides a system for predicting battery life based on the health of a vehicle battery.

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application.

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. In the description of this application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of this application. Unless otherwise clearly defined and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0040] See Figure 1 As shown, a method for predicting battery life based on vehicle battery health provided by an embodiment of the present invention specifically includes the following steps:

[0041] S1: Obtain the mileage and SOH (State Of Health) data during vehicle operation, and obtain a mileage-SOH scatter plot based on the obtained mileage and SOH data;

[0042] In the embodiment of the present invention, during vehicle operation, the running data is sent to the vehicle enterprise big data monitoring platform through a T-box (Telematics BOX). The running data includes the mileage and SOH data during vehicle operation. That is, during vehicle operation, the running data is sent to the vehicle enterprise big data monitoring platform in real time through the T-box, and there is a corresponding relationship between the mileage and SOH in the running data.

[0043] Based on the mileage and SOH data, the vehicle enterprise big data monitoring platform plots a scatter distribution graph of mileage-SOH with mileage as the abscissa and the SOH corresponding to the mileage as the ordinate. That is, in the coordinate system, with mileage as the abscissa and the SOH value corresponding to the mileage as the ordinate, a point is obtained. There are multiple mileage and SOH data in the vehicle enterprise big data monitoring platform, so multiple points can be obtained in the coordinate system to form a scatter distribution graph of mileage-SOH. The unit of the abscissa of the scatter distribution graph of mileage-SOH is 10,000 kilometers, and the unit of the ordinate is a percentage.

[0044] S2: In the obtained scatter distribution graph of mileage-SOH, select a preset number of points according to the set rules as the fitting reference points;

[0045] In the embodiment of the present invention, a preset number of points are selected according to the set rules as the fitting reference points, specifically: after the starting mileage, a preset number of points are selected according to the set rules as the fitting reference points; the starting mileage is the mileage that the vehicle has accumulated when the battery activation period ends.

[0046] For a battery, there is an activation period at the initial stage of the cycle, and the capacity slowly increases during this period. The activation lasts for about 20-50 cycles. The mileage that the vehicle has accumulated when the battery activation period ends is used as the starting mileage, and the data of the total vehicle mileage greater than the starting mileage is used for further data processing subsequently.

[0047] In the embodiment of the present invention, the set rules are specifically:

[0048] S201: On the horizontal axis of the scatter distribution graph of mileage-SOH, select a preset number of points at a fixed interval after the starting mileage as the reference points;

[0049] S202: Based on the scatter distribution graph of mileage-SOH, select a set number of points near each reference point and obtain the SOH value of each point;

[0050] S203: For each reference point, sort the SOH values of the set number of points near the current reference point from high to low, and determine the points with the top preset percentage as the fitting reference points corresponding to the current reference point.

[0051] The set rules of the present invention are specifically described below.

[0052] Suppose the battery health target achievement rate of a certain vehicle model is 80% (i.e., the preset percentage value is 80%), and it is required to evaluate the corresponding warranty mileage. The preset number is 5, and the set number is 50. In the actual application process, the number of reference points and the point selection range should be adjusted according to the vehicle quantity and vehicle mileage distribution. Generally, the principle of selecting as many points as possible and making the reference points as scattered as possible should be followed, so as to make the data representative and accurate at the same time.

[0053] First, on the horizontal axis of the mileage-SOH scatter plot, select 5 abscissa points (i.e., reference points) at fixed intervals after the starting mileage, and the intervals between these 5 abscissa points are fixed.

[0054] Then, for the first selected abscissa point, select 50 points in the mileage-SOH scatter plot near the first abscissa point, and the abscissa values of these 50 points are near the abscissa value of the first point.

[0055] Finally, sort the SOH values of the 50 points near the first abscissa point from high to low, and determine the points ranked in the top 80% (i.e., the 40th) as the fitting reference points corresponding to the first abscissa point. Similarly, determine the fitting reference points corresponding to the 2nd, 3rd, 4th, and 5th abscissa points in turn, and then perform fitting according to the coordinates of the determined 5 fitting reference points to obtain the attenuation curve.

[0056] Assume that the first reference point is X0, the second reference point is X1, the third reference point is X2, the fourth reference point is X3, and the fifth reference point is X4. Then, these 5 reference points satisfy X1 = X0 + 1 / 5(X - X0), X2 = X0 + 2 / 5(X - X0), X3 = X0 + 3 / 5(X - X0), X4 = X0 + 4 / 5(X - X0).

[0057] S3: Based on the coordinates of the selected fitting reference points, perform fitting to obtain an attenuation curve representing the relationship between mileage and SOH.

[0058] S4: According to the obtained attenuation curve, calculate the mileage corresponding to the preset percentage value of SOH, and the calculated mileage is the warranty mileage when the SOH achievement rate is the preset percentage value.

[0059] Assume that the battery health target achievement rate of a certain vehicle model is 80%, and it is required to evaluate the corresponding warranty mileage. Then calculate the mileage when the SOH is 80%, and the warranty mileage when the battery health target achievement rate reaches 80% is obtained to realize the prediction of the battery warranty life achievement rate.

[0060] In a possible implementation manner, the prediction of the battery health achievement rate can also be realized according to the relevant fitting curve equation.

[0061] For example, the battery warranty requirement of a certain listed vehicle model is that the battery SOH ≥ 80% within n ten thousand kilometers, and it is required to predict the battery health achievement rate.

[0062] Based on the existing battery health data, a scatter plot of mileage-SOH is obtained. After selecting the fitting curve type according to the battery characteristics, the fitting curve equation is obtained based on the known points (0, 100%) and (n, 80%). The proportion of the scatter points located above the equation is the battery health attainment rate. If the attainment rate fails to meet expectations, the battery management system program can be updated in a timely manner to extend the battery life.

[0063] In the embodiments of the present invention, the capacity attenuation curve of the battery cell itself depends on the material system and the design scheme. According to the battery cell attenuation curve (within the warranty period or the equivalent vehicle scrapping period), the fitting function type used for the vehicle attenuation curve is determined, such as a linear function, a quadratic function, an exponential function, etc.

[0064] The following combines an example to specifically illustrate the method for predicting the battery life based on the vehicle battery health of the present invention.

[0065] After cost accounting and market maintenance cost calculation for a certain vehicle model, the attainment rate of the battery health is required to be 90%, and the battery health requirement is 80%. It is required to evaluate the corresponding warranty mileage.

[0066] According to the cell cycle data, it is known that the attenuation curve is approximately a linear function, and the activation period is about 10,000 km. X0 = 10,000 can be selected. According to the health distribution data, a reference point is selected every 10,000 km. X1 = 20,000, X2 = 30,000, X3 = 40,000, X4 = 50,000, X5 = 60,000, X7 = 70,000. The health data within ±200 km is selected near these 7 points, and the 90% point from high to low of each point is obtained. Then, the fitting reference point coordinates are further obtained as (10,000, 98.7), (20,000, 97.09), (30,000, 96.44), (40,000, 96.2), (50,000, 96.34), (60,000, 96), (70,000, 94.17). The attenuation equation Y(X) = 98.585 - 5.26 * 10 -5 x (see Figure 2 ), when the health Y takes 80%, X = 353326 km, that is, the warranty mileage X 质保 = 353326 km.

[0067] The method for predicting battery life based on the health status of a vehicle battery according to an embodiment of the present invention obtains mileage and SOH data during the operation of the vehicle, and obtains a mileage-SOH scatter plot based on the obtained mileage and SOH data. Then, in the obtained mileage-SOH scatter plot, a preset number of points are selected according to a set rule as fitting reference points. Then, based on the coordinates of the selected fitting reference points, fitting is performed to obtain an attenuation curve representing the relationship between mileage and SOH. Then, according to the obtained attenuation curve, the mileage corresponding to the SOH being a preset percentage value is calculated, and the calculated mileage is the warranty mileage with the SOH achievement rate being the preset percentage value. By predicting the overall battery life of the vehicle through the health status data of the actually used battery, the battery life can be predicted without using complex calculation methods, effectively evaluating the qualification rate of the battery during the warranty period, and providing necessary data support for the subsequent software strategy update of vehicle manufacturers.

[0068] In a possible implementation manner, an embodiment of the present invention further provides a readable storage medium located in a PLC (Programmable Logic Controller) controller. A computer program is stored on the readable storage medium, and when the program is executed by a processor, the steps of the method for predicting battery life based on the health status of a vehicle battery described below are implemented:

[0069] Obtain mileage and SOH data during the operation of the vehicle, and obtain a mileage-SOH scatter plot based on the obtained mileage and SOH data;

[0070] In the obtained mileage-SOH scatter plot, select a preset number of points according to a set rule as fitting reference points;

[0071] Based on the coordinates of the selected fitting reference points, perform fitting to obtain an attenuation curve representing the relationship between mileage and SOH;

[0072] According to the obtained attenuation curve, calculate the mileage corresponding to the SOH being a preset percentage value, and the calculated mileage is the warranty mileage with the SOH achievement rate being the preset percentage value.

[0073] The storage medium may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0074] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0075] The computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0076] A system for predicting battery life based on vehicle battery health provided by an embodiment of the present invention includes an acquisition module, a selection module, a fitting module, and a calculation module.

[0077] The acquisition module is used to acquire the mileage and SOH data during the operation of the vehicle, and obtain a mileage-SOH scatter plot based on the acquired mileage and SOH data; the selection module is used to select a preset number of points as fitting reference points according to the set rules in the obtained mileage-SOH scatter plot; the fitting module is used to perform fitting based on the coordinates of the selected fitting reference points to obtain an attenuation curve representing the relationship between mileage and SOH; the calculation module is used to calculate the mileage corresponding to the SOH being a preset percentage value according to the obtained attenuation curve, and the calculated mileage is the warranty mileage when the SOH achievement rate is the preset percentage value. By predicting the overall battery life of the vehicle through the health data of the actually used battery, the battery life can be predicted without using complex calculation methods, effectively evaluating the qualification rate of the battery during the warranty period, and providing necessary data support for the subsequent software strategy update of the vehicle enterprise.

[0078] In the embodiment of the present invention, during the operation of the vehicle, the operation data is sent to the big data monitoring platform of the vehicle enterprise through the T-box, and the operation data includes the mileage and SOH data during the operation of the vehicle. The big data monitoring platform of the vehicle enterprise makes a graph with the mileage as the abscissa and the SOH corresponding to the mileage as the ordinate according to the mileage and SOH data to obtain a mileage-SOH scatter plot.

[0079] That is, during the operation of the vehicle, the operation data is sent to the big data monitoring platform of the vehicle enterprise in real time through the T-box, and there is a corresponding relationship between the mileage and SOH in the operation data.

[0080] That is, in the coordinate system, with the mileage as the abscissa and the SOH value corresponding to the mileage as the ordinate, a point is obtained. There are multiple mileage and SOH data in the big data monitoring platform of the vehicle enterprise, so multiple points can be obtained in the coordinate system to form a mileage-SOH scatter plot. The unit of the abscissa of the mileage-SOH scatter plot is 10,000 kilometers, and the unit of the ordinate is a percentage.

[0081] In the embodiment of the present invention, a preset number of points are selected as fitting reference points according to the set rules, specifically: after the starting mileage, a preset number of points are selected as fitting reference points according to the set rules; the starting mileage is the mileage that the vehicle has accumulated when the battery activation period ends.

[0082] In the embodiment of the present invention, the set rules are specifically:

[0083] On the horizontal axis of the mileage-SOH scatter plot, a preset number of points are selected at a fixed interval after the starting mileage as the reference points;

[0084] Based on the mileage-SOH scatter plot, a set number of points are selected near each reference point, and the SOH value of each point is obtained;

[0085] For each reference point, sort the SOH values of a set number of points near the current reference point from high to low, and determine the fitting reference points corresponding to the current reference point for the top preset percentage of the ranked points.

[0086] The following specifically describes the setting rules of the present invention.

[0087] Suppose the battery health target achievement rate of a certain vehicle model is 80% (i.e., the preset percentage value is 80%), the required evaluation of the corresponding warranty mileage, the preset number is 5, and the set number is 50.

[0088] First, on the horizontal axis of the mileage-SOH scatter plot, select 5 abscissa points (i.e., reference points) at a fixed interval after the starting mileage, and there is a fixed interval between these 5 abscissa points;

[0089] Then, for the first selected abscissa point, select 50 points in the mileage-SOH scatter plot near the first abscissa point, and the abscissa values of these 50 points are near the abscissa value of the first point;

[0090] Finally, sort the SOH values of the 50 points near the first abscissa point from high to low, and determine the point ranked in the top 80% (i.e., the 40th) as the fitting reference point corresponding to the first abscissa point. Similarly, determine the fitting reference points corresponding to the 2nd, 3rd, 4th, and 5th abscissa points in turn, and then perform fitting based on the coordinates of the determined 5 fitting reference points to obtain the decay curve.

[0091] The system for predicting battery life based on vehicle battery health in the embodiment of the present invention obtains the mileage and SOH data during vehicle operation, obtains the mileage-SOH scatter plot based on the obtained mileage and SOH data, then selects a preset number of points in the obtained mileage-SOH scatter plot according to the setting rules as fitting reference points, and then performs fitting based on the coordinates of the selected fitting reference points to obtain a decay curve representing the relationship between mileage and SOH. Then, according to the obtained decay curve, calculate the mileage corresponding to the SOH value of the preset percentage. The calculated mileage is the warranty mileage when the SOH achievement rate is the preset percentage. By predicting the overall battery life of the vehicle through the existing health data of the actually used battery, the battery life can be predicted without using complex calculation methods, effectively evaluating the qualification rate of the battery during the warranty period and providing necessary data support for the subsequent software strategy update of vehicle manufacturers.

[0092] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features claimed herein.

[0093] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in Figure 1 one or more of the flows or multiple flows and / or blocks Figure 1 one or more of the blocks or multiple blocks.

Claims

1. A method for predicting battery life based on the health of a vehicle battery, characterized in that, Specifically, it includes the following steps: Obtain the mileage and SOH data during the vehicle operation, and obtain a mileage - SOH scatter plot based on the obtained mileage and SOH data; In the obtained mileage - SOH scatter plot, select a preset number of points according to the set rules as the fitting reference points; Based on the coordinates of the selected fitting reference points, perform fitting to obtain a decay curve representing the relationship between mileage and SOH; According to the obtained decay curve, calculate the mileage corresponding to when the SOH is a preset percentage value, and the calculated mileage is the warranty mileage with the SOH achievement rate being the preset percentage value; Among them, there are multiple mileage and SOH data in the vehicle enterprise big data monitoring platform, so multiple points can be obtained in the coordinate system to form a mileage - SOH scatter plot; Among them, the set rules are specifically: On the horizontal axis of the mileage - SOH scatter plot, select a preset number of points at a fixed interval after the starting mileage as the reference points; Based on the mileage - SOH scatter plot, select a set number of points near each reference point and obtain the SOH value of each point; For each reference point, sort the points according to the SOH values of the set number of points near the current reference point from high to low, and determine the points with the top preset percentage value as the fitting reference points corresponding to the current reference point, and the preset percentage value is the SOH achievement rate.

2. The method for predicting battery life based on the health of a vehicle battery according to claim 1, characterized in that: During the vehicle operation, the running data including the mileage and SOH data during the vehicle operation is sent to the vehicle enterprise big data monitoring platform through the T - box.

3. The method for predicting battery life based on the health of a vehicle battery according to claim 2, characterized in that: The vehicle enterprise big data monitoring platform makes a graph with the mileage as the abscissa and the SOH corresponding to the mileage as the ordinate according to the mileage and SOH data to obtain a mileage - SOH scatter plot.

4. The method for predicting battery life based on the health of a vehicle battery according to claim 3, characterized in that: The step of selecting a preset number of points as the fitting reference points according to the set rules is specifically: after the starting mileage, select a preset number of points according to the set rules as the fitting reference points; The starting mileage is the mileage that the vehicle has accumulated when the battery activation period ends.

5. A system for predicting battery life based on the health of a vehicle battery, characterized in that, It includes: An acquisition module, which is used to obtain the mileage and SOH data during the vehicle operation, and obtain a mileage - SOH scatter plot based on the obtained mileage and SOH data; A selection module, which is used to select a preset number of points in the obtained mileage - SOH scatter plot as the fitting reference points according to the set rules; A fitting module, which is used to perform fitting based on the coordinates of the selected fitting reference points to obtain a decay curve representing the relationship between mileage and SOH; A calculation module, which is used to calculate the mileage corresponding to when the SOH is a preset percentage value according to the obtained decay curve, and the calculated mileage is the warranty mileage with the SOH achievement rate being the preset percentage value; Among them, there are multiple mileage and SOH data in the vehicle enterprise big data monitoring platform, so multiple points can be obtained in the coordinate system to form a mileage - SOH scatter plot; Among them, the set rules are specifically: On the horizontal axis of the mileage - SOH scatter plot, select a preset number of points at a fixed interval after the starting mileage as the reference points; Based on the mileage-SOH scatter plot, a set number of points are selected near each reference point, and the SOH value of each point is obtained. For each reference point, according to the SOH values of the set number of points near the current reference point, they are sorted from high to low, and the points with the top preset percentage value in the ranking are determined as the fitting reference points corresponding to the current reference point. The preset percentage value is the SOH achievement rate.

6. The system for predicting battery life based on the health of a vehicle battery according to claim 5, characterized in that: During the vehicle operation, the running data including the mileage and SOH data during the vehicle operation are sent to the vehicle enterprise big data monitoring platform through the T-box.

7. The system for predicting battery life based on the health of a vehicle battery according to claim 6, characterized in that: The vehicle enterprise big data monitoring platform makes a graph with the mileage as the abscissa and the SOH corresponding to the mileage as the ordinate based on the mileage and SOH data to obtain the mileage-SOH scatter plot.

8. The system for predicting battery life based on the health of a vehicle battery according to claim 7, characterized in that: The preset number of points are selected as the fitting reference points according to the set rules, specifically: after the starting mileage, the preset number of points are selected as the fitting reference points according to the set rules. The starting mileage is the mileage that the vehicle has accumulated when the battery activation period ends.

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