CN118330474B - New energy automobile battery module detection method - Google Patents
New energy automobile battery module detection method Download PDFInfo
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- CN118330474B CN118330474B CN202410773169.4A CN202410773169A CN118330474B CN 118330474 B CN118330474 B CN 118330474B CN 202410773169 A CN202410773169 A CN 202410773169A CN 118330474 B CN118330474 B CN 118330474B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The invention discloses a new energy automobile battery module detection method, which belongs to the technical field of battery modules and specifically comprises the following steps: collecting operation parameters of the battery module at fixed intervals, and recording the health degree of the battery during each collection; calculating the increasing speed of each operation parameter accumulated value when each operation parameter is collected, calculating the average increasing speed, overlapping the current operation parameter value with the average increasing speed, inputting a battery health degree prediction model, outputting the corresponding battery health degree until the battery health degree is lower than the standard lower limit, obtaining the corresponding overlapping times, calculating the predicted duration according to the collection interval, comparing the predicted duration with the expected use duration of a user, and prompting the user when the predicted duration is lower than the expected use duration; the invention realizes the long-term prediction of the battery health.
Description
Technical Field
The invention relates to the technical field of battery modules, in particular to a new energy automobile battery module detection method.
Background
The battery module in the new energy automobile is a core component of the whole automobile, provides a power source of the whole automobile, and the performance and the health condition of the battery module directly influence the endurance mileage, the safety and the overall reliability of the automobile. Therefore, it is important to accurately and effectively detect and evaluate the health degree of the battery module of the new energy automobile.
The battery health is the ratio between the actual stored power and the battery set stored power. The higher the ratio, the better the battery health. The battery health is affected by a number of factors including the number of charge and discharge cycles, the temperature used, the rate of charge, and the aging of the battery itself. Frequent charge and discharge, use in high temperature environments, and improper charging may accelerate the decrease in battery health, thereby affecting the overall performance and service life of the battery.
With the development of technology, more and more devices are built with a battery health detection function, and a user can directly check the health state of a battery in a setting. Meanwhile, battery detection tools and applications of third parties are also available on the market, so that users can be helped to know the battery conditions more accurately. But only can display the battery state, the future development condition of the battery health degree can not be predicted according to the existing battery service condition, and the user can not be prompted to adjust the existing battery service habit, so that the service life of the battery is prolonged.
Disclosure of Invention
The invention aims to provide a new energy automobile battery module detection method, which solves the following technical problems:
The prior art can only display the battery state, but can not predict the future development condition of the battery health according to the current battery service condition, and can not prompt a user to adjust the current battery service habit, thereby prolonging the service life of the battery.
The aim of the invention can be achieved by the following technical scheme:
a new energy automobile battery module detection method comprises the following steps:
collecting operation parameters of the battery module at fixed intervals, wherein the operation parameters comprise charge and discharge cycle times m, temperature T and charge rate v, and recording the health degree of the battery during each collection;
Filtering the operation parameters, calculating the absolute value delta T of the difference between the temperature T and the proper temperature interval of the battery, generating a time-sequence-ordered absolute value sequence of the difference and a time-sequence-ordered absolute value sequence of the charge rate, respectively accumulating and summing the sequences, wherein when the acquisition time is n, the accumulated value of the corresponding absolute value of the temperature difference is The accumulated value of the charge rate isThe accumulated value of the cycle times is the self;
Constructing a battery health degree prediction model based on a transducer deep learning network, inputting the accumulated values of all the operation parameters and the corresponding battery health degrees into the transducer deep learning network for training, outputting the fitting relation between the accumulated values of the operation parameters and the battery health degrees, verifying the fitting relation, and repeatedly updating to obtain a verified battery health degree prediction model;
Calculating the increasing speed of each operation parameter accumulated value when each operation parameter is collected, calculating the average increasing speed, overlapping the current operation parameter value with the average increasing speed, overlapping the current operation parameter value by 1,2, and N times in sequence, inputting the overlapped operation parameters into a battery health degree prediction model, outputting the corresponding battery health degree until the battery health degree is lower than the standard lower limit, obtaining the corresponding overlapping times N, calculating the predicted duration according to the collection interval, comparing the predicted duration with the expected use duration of a user, and prompting the user when the predicted duration is lower than the expected use duration.
As a further scheme of the invention: the specific calculation process of the absolute value delta T of the difference value is as follows:
When the temperature T is lower than the lower limit of the battery proper temperature interval, marking the absolute value of the difference between the temperature T and the lower limit of the battery proper temperature interval as delta T; when the temperature T is higher than the upper limit of the battery suitable temperature interval, the absolute value of the difference between the temperature T and the upper limit of the battery suitable temperature interval is marked as Δt.
As a further scheme of the invention: the specific process of the operation parameter filtration is as follows:
And processing the operation parameter sequence by adopting a first-order differential exponential smoothing algorithm and a second-order differential exponential smoothing model in sequence, and replacing an abnormal value by median filtering.
As a further scheme of the invention: the verification process of the battery health degree prediction model comprises the following steps:
Establishing a plane rectangular coordinate system, respectively taking an accumulated value T 'n of an absolute value of an operating temperature difference value, a charge rate accumulated value v' n and charge and discharge cycle times m as independent variables, and taking battery health outputted by a battery health prediction model as dependent variables to carry out one-dimensional regression fitting, wherein the formula is as follows: y=a jxj+bj;, wherein a j and b j are function coefficients, j=1, 2,3, x j respectively represent an accumulated value of absolute values of temperature difference values, a charge rate accumulated value and charge and discharge cycle times, y represents battery health, and a solution of a j and b j is calculated through a least square method;
based on the solution of the function coefficients, the correlation coefficient r of the television capacity prediction model is calculated by the following formula: And calculating to obtain a correlation coefficient r 1、r2、r3、r4、r5、r6, if all the correlation coefficients are more than or equal to 0.8, indicating that the model is qualified, and if the correlation coefficients are less than 0.8, training the model again based on a transform deep learning network.
As a further scheme of the invention: the definition of the charge-discharge cycle number is a complete process of charging the display battery capacity from 0% to 100% and discharging to 0%, and the display battery capacity is the battery capacity displayed by the automobile system.
As a further scheme of the invention: the battery health degree is the ratio of the actual battery energy stored after the current battery is fully charged to the rated stored battery energy when leaving the factory.
As a further scheme of the invention: the charge rate is in kilowatts.
The invention has the beneficial effects that:
According to the method, the state and the expected service life of the battery can be more accurately estimated by collecting and analyzing the key operation parameters of the battery module and combining the real-time data of the battery health degree; the accuracy of prediction is further improved by using advanced deep learning models, because the models can effectively process time series data and capture long-term dependency; by predicting future changes in battery health, the method can identify potential performance degradation or failure risk of the battery in advance, thereby allowing an owner or serviceman to intervene before the problem actually occurs, which helps to reduce safety risks and maintenance costs caused by sudden failures.
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The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a new energy automobile battery module detection method of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a new energy automobile battery module detection method, which comprises the following steps:
collecting operation parameters of the battery module at fixed intervals, wherein the operation parameters comprise charge and discharge cycle times m, temperature T and charge rate v, and recording the health degree of the battery during each collection;
Filtering the operation parameters, calculating the absolute value delta T of the difference between the temperature T and the proper temperature interval of the battery, generating a time-sequence-ordered absolute value sequence of the difference and a time-sequence-ordered absolute value sequence of the charge rate, respectively accumulating and summing the sequences, wherein when the acquisition time is n, the accumulated value of the corresponding absolute value of the temperature difference is The accumulated value of the charge rate isThe accumulated value of the cycle times is the self;
Constructing a battery health degree prediction model based on a transducer deep learning network, inputting the accumulated values of all the operation parameters and the corresponding battery health degrees into the transducer deep learning network for training, outputting the fitting relation between the accumulated values of the operation parameters and the battery health degrees, verifying the fitting relation, and repeatedly updating to obtain a verified battery health degree prediction model;
Calculating the increasing speed of each operation parameter accumulated value when each operation parameter is collected, calculating the average increasing speed, overlapping the current operation parameter value with the average increasing speed, overlapping the current operation parameter value by 1,2, and N times in sequence, inputting the overlapped operation parameters into a battery health degree prediction model, outputting the corresponding battery health degree until the battery health degree is lower than the standard lower limit, obtaining the corresponding overlapping times N, calculating the predicted duration according to the collection interval, comparing the predicted duration with the expected use duration of a user, and prompting the user when the predicted duration is lower than the expected use duration.
The method can evaluate the state and the life expectancy of the battery more accurately by collecting and analyzing the key operation parameters of the battery module and combining the real-time data of the battery health degree.
The use of advanced deep learning models further improves the accuracy of the predictions because these models can effectively process time series data capturing long-term dependencies.
By predicting future changes in battery health, the method can identify potential performance degradation or failure risk of the battery in advance, thereby allowing an owner or serviceman to intervene before the problem actually occurs, which helps to reduce safety risks and maintenance costs caused by sudden failures.
In another preferred embodiment of the present invention, the specific calculation process of the absolute value Δt of the difference is:
When the temperature T is lower than the lower limit of the battery proper temperature interval, marking the absolute value of the difference between the temperature T and the lower limit of the battery proper temperature interval as delta T; when the temperature T is higher than the upper limit of the battery suitable temperature interval, the absolute value of the difference between the temperature T and the upper limit of the battery suitable temperature interval is marked as Δt.
In another preferred embodiment of the present invention, the specific process of operating parameter filtering is:
And processing the operation parameter sequence by adopting a first-order differential exponential smoothing algorithm and a second-order differential exponential smoothing model in sequence, and replacing an abnormal value by median filtering.
In another preferred embodiment of the present invention, the verification process of the battery health prediction model is:
Establishing a plane rectangular coordinate system, respectively taking an accumulated value T 'n of an absolute value of an operating temperature difference value, a charge rate accumulated value v' n and charge and discharge cycle times m as independent variables, and taking battery health outputted by a battery health prediction model as dependent variables to carry out one-dimensional regression fitting, wherein the formula is as follows: y=a jxj+bj;, wherein a j and b j are function coefficients, j=1, 2,3, x j respectively represent an accumulated value of absolute values of temperature difference values, a charge rate accumulated value and charge and discharge cycle times, y represents battery health, and a solution of a j and b j is calculated through a least square method;
based on the solution of the function coefficients, the correlation coefficient r of the television capacity prediction model is calculated by the following formula: And calculating to obtain a correlation coefficient r 1、r2、r3、r4、r5、r6, if all the correlation coefficients are more than or equal to 0.8, indicating that the model is qualified, and if the correlation coefficients are less than 0.8, training the model again based on a transform deep learning network.
By computing multiple correlation coefficients, the method is able to comprehensively evaluate the performance of the model from multiple dimensions. If some correlation coefficients are found to be not up to standard, the model can be adjusted and optimized in a targeted manner, so that the generalization capability and the prediction precision of the model are improved
In another preferred embodiment of the present invention, the number of charge-discharge cycles is defined as a complete process of charging the display battery capacity from 0% to 100% and discharging to 0%, where the display battery capacity is the battery capacity displayed by the automobile system.
In another preferred embodiment of the present invention, the battery health is the ratio of the stored actual battery energy after the current battery is fully charged to the rated stored battery energy at the time of shipment.
In another preferred embodiment of the invention, the charge rate is in units of kilowatts absolute, and the effect on battery health is more pronounced relative to the relative unit percentages.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (7)
1. The new energy automobile battery module detection method is characterized by comprising the following steps of:
collecting operation parameters of the battery module at fixed intervals, wherein the operation parameters comprise charge and discharge cycle times m, temperature T and charge rate v, and recording the health degree of the battery during each collection; filtering the operation parameters, calculating the absolute value delta T of the difference between the temperature T and the proper temperature interval of the battery, generating a time-sequence-ordered absolute value sequence of the difference and a time-sequence-ordered absolute value sequence of the charge rate, respectively accumulating and summing the sequences, wherein when the acquisition time is n, the accumulated value of the corresponding absolute value of the temperature difference is The accumulated value of the charge rate isThe accumulated value of the cycle times is the self; constructing a battery health degree prediction model based on a transducer deep learning network, inputting the accumulated values of all the operation parameters and the corresponding battery health degrees into the transducer deep learning network for training, outputting the fitting relation between the accumulated values of the operation parameters and the battery health degrees, verifying the fitting relation, and repeatedly updating to obtain a verified battery health degree prediction model;
Calculating the increasing speed of each operation parameter accumulated value when each operation parameter is collected, calculating the average increasing speed, overlapping the current operation parameter value with the average increasing speed, overlapping the current operation parameter value by 1,2, and N times in sequence, inputting the overlapped operation parameters into a battery health degree prediction model, outputting the corresponding battery health degree until the battery health degree is lower than the standard lower limit, obtaining the corresponding overlapping times N, calculating the predicted duration according to the collection interval, comparing the predicted duration with the expected use duration of a user, and prompting the user when the predicted duration is lower than the expected use duration.
2. The method for detecting a battery module of a new energy automobile according to claim 1, wherein the specific calculation process of the absolute value Δt of the difference is: when the temperature T is lower than the lower limit of the battery proper temperature interval, marking the absolute value of the difference between the temperature T and the lower limit of the battery proper temperature interval as delta T; when the temperature T is higher than the upper limit of the battery suitable temperature interval, the absolute value of the difference between the temperature T and the upper limit of the battery suitable temperature interval is marked as Δt.
3. The method for detecting the battery module of the new energy automobile according to claim 1, wherein the specific process of filtering the operation parameters is as follows:
And processing the operation parameter sequence by adopting a first-order differential exponential smoothing algorithm and a second-order differential exponential smoothing model in sequence, and replacing an abnormal value by median filtering.
4. The method for detecting the battery module of the new energy automobile according to claim 1, wherein the verification process of the battery health prediction model is as follows:
Establishing a plane rectangular coordinate system, respectively taking an accumulated value T 'n of an absolute value of an operating temperature difference value, a charge rate accumulated value v' n and charge and discharge cycle times m as independent variables, and taking battery health outputted by a battery health prediction model as dependent variables to carry out one-dimensional regression fitting, wherein the formula is as follows: y=a jxj+bj;, wherein a j and b j are function coefficients, j=1, 2,3, x j respectively represent an accumulated value of absolute values of temperature difference values, a charge rate accumulated value and charge and discharge cycle times, y represents battery health, and a solution of a j and b j is calculated through a least square method;
Based on the solution of the function coefficients, the correlation coefficient r of the battery capacity prediction model is calculated by the following formula: And calculating to obtain a correlation coefficient r 1、r2、r3、r4、r5、r6, if all the correlation coefficients are more than or equal to 0.8, indicating that the model is qualified, and if the correlation coefficients are less than 0.8, training the model again based on a transform deep learning network.
5. The method for detecting a battery module of a new energy automobile according to claim 1, wherein the definition of the charge-discharge cycle number is a complete process of charging the display battery capacity from 0% to 100% and discharging the display battery capacity to 0%, and the display battery capacity is the battery capacity displayed by the automobile system.
6. The method for detecting the battery module of the new energy automobile according to claim 1, wherein the battery health degree is a ratio of actual battery energy stored after the current battery is fully charged to rated stored battery energy when leaving a factory.
7. The method for detecting a battery module of a new energy automobile according to claim 1, wherein the charging rate is in kilowatts.
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| CN107839500A (en) * | 2017-07-11 | 2018-03-27 | 苏州大学 | A kind of dynamic corrections SOC lithium battery group balance control method and system |
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| CN108448665A (en) * | 2018-03-08 | 2018-08-24 | 合肥新胜电达电子科技有限公司 | A kind of intelligent charging system that accumulator group is charged and conserved |
| CN110058159A (en) * | 2019-04-29 | 2019-07-26 | 杭州电子科技大学 | A kind of lithium battery health status estimation method based on grey neural network |
| CN110161425B (en) * | 2019-05-20 | 2020-05-19 | 华中科技大学 | Method for predicting remaining service life based on lithium battery degradation stage division |
| US11768249B2 (en) * | 2020-10-29 | 2023-09-26 | Toyota Research Institute, Inc. | Systems and methods for predicting battery life using data from a diagnostic cycle |
| CN112765772B (en) * | 2020-12-25 | 2022-11-04 | 武汉理工大学 | Power battery residual life prediction method based on data driving |
| CN113759269B (en) * | 2021-11-10 | 2022-02-25 | 武汉理工大学 | Method and system for monitoring health state of battery of electric vehicle |
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| CN101635470A (en) * | 2009-08-19 | 2010-01-27 | 王广生 | Quick charger of power-saving storage battery and intelligent charging method |
| CN107839500A (en) * | 2017-07-11 | 2018-03-27 | 苏州大学 | A kind of dynamic corrections SOC lithium battery group balance control method and system |
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