CN114609529B - Data-driven lithium ion battery remaining life prediction method and system - Google Patents
Data-driven lithium ion battery remaining life prediction method and system Download PDFInfo
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- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 32
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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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
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- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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- G—PHYSICS
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- 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]
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Abstract
The invention relates to the field of lithium ion batteries, and is used for solving the problems that the existing lithium ion battery residual life prediction method has high detection cost, long detection time and inconvenient geographical position, and the prediction result of the lithium ion battery residual life is seriously distorted as the data acquired by a new energy automobile is more complex, in particular to a data-driven lithium ion battery residual life prediction method and system.
Description
Technical Field
The invention relates to the field of lithium ion batteries, in particular to a data-driven lithium ion battery residual life prediction method and system.
Background
In recent years, along with the rising of electric surge of automobiles, various large traditional automobile enterprises put on the body for the research and development of electric automobile products and the market angle, along with the continuous innovation of electric automobile product technology, the product content is continuously rich, and the acceptance of consumers to the electric automobile products is also continuously improved;
the residual life of the lithium ion battery means that the lithium ion battery is continuously charged and discharged in certain specific environments and requirements, when the residual capacity reaches a specified failure broad value, the battery can be judged to be failed, and the residual capacity of the power battery is one of hot topics in the industry;
In the process of service life degradation of the lithium ion battery, once the degradation process is ignored, lithium ions are used in an overrun and overload way, the battery and instrument equipment of the new energy automobile are damaged slightly, serious fault accidents are even caused, the traditional method generally uses special equipment for in-line detection, the detection cost is high, the detection time is long, the geographic position is inconvenient and the like, and some high-tech new energy automobiles can monitor and analyze the running condition of the new energy automobile in real time through an automobile processor, so that the residual service life of the lithium ion battery is predicted, the purpose of accurate prediction can be achieved, but the calculation capability of the existing automobile processor is not strong enough, the acquired data is more complicated and difficult to process along with the longer service time of the new energy automobile, and the predicted result of the residual service life of the lithium ion battery is seriously distorted;
How to efficiently, conveniently, timely and accurately predict the residual life of the lithium ion battery of the new energy automobile is the key of the invention, so that a data-driven lithium ion battery residual life prediction method and system are needed to solve the problems.
Disclosure of Invention
In order to overcome the technical problems described above, the present invention is directed to a data-driven lithium ion battery remaining life prediction method and system: the method comprises the steps that an electric quantity data packet and a state data packet are sent to a final selection server through a processor, the final selection server obtains rough measured electric quantity and transmission time difference according to the electric quantity data packet, an adjustment value is obtained according to the state data packet and the transmission time difference, the rough measured electric quantity is corrected according to the adjustment value to obtain accurate measured electric quantity, the accurate measured electric quantity is sent to the processor and a database, and finally the battery remaining life condition is obtained, so that the problems that an existing lithium ion battery remaining life prediction method is high in detection cost, long in detection time and inconvenient in geographic position, and the prediction result of the lithium ion battery remaining life is seriously distorted due to the fact that data acquired along with a new energy automobile are more complex are solved.
The aim of the invention can be achieved by the following technical scheme:
A data-driven lithium ion battery residual life prediction method comprises the following steps:
Step one: the method comprises the steps that an electric quantity acquisition module acquires battery information of a new energy automobile, acquires charging limit voltage, terminating discharging voltage and battery production time of the battery, monitors time required by the battery to reach the charging limit voltage after the battery reaches the terminating discharging voltage through a voltage monitoring unit, marks the time as an electric supplementing time difference BS, acquires current in charging in real time through a current acquisition unit, calculates an average value after removing current with the largest value and current with the smallest value, acquires electric supplementing current BL, acquires environmental temperature in charging in real time through a temperature acquisition unit, calculates the average value, and acquires electric supplementing ring temperature BW, and the electric quantity acquisition module combines the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW to form an electric quantity data packet to be sent to a processor, and sends the battery production time to a state analysis module;
step two: the processor receives the electric quantity data packet, generates a selection analysis instruction and a state analysis instruction, sends the selection analysis instruction to the selection analysis module, and sends the state analysis instruction to the state analysis module;
Step three: the selection analysis module acquires the sending position of the selection analysis instruction after receiving the selection analysis instruction, simultaneously acquires the positions of a plurality of servers, acquires the number of data packets in each server and the total size of all the data packets and marks the number of the data packets SL and the number of the data packets SZ respectively, marks the distance between the positions of the plurality of servers and the sending position of the selection analysis instruction as a time difference SJ in sequence, normalizes the number of the data packets SL, the number of the data packets SZ and the time difference SJ, and substitutes the normalized data packets into a formula Obtaining a selected figure of merit XY, wherein q1, q2 and q3 are all preset weight coefficients, q1+q2+q3=1, taking q1=0.24, q2=0.26 and q3=0.5, marking a server corresponding to the maximum selected figure of merit XY as a final selected server, generating a communication connection instruction according to the final selected server, and sending the communication connection instruction to a processor;
Step four: the state analysis module receives a state analysis instruction, acquires the driving distance and the driving time of the new energy automobile, marks the driving distance and the driving time as a line space value XJ and a line space value XS respectively, marks the battery production time as an electric output value DC, and then combines the line space value XJ, the line space value XS and the electric output value DC to form a state data packet and sends the state data packet to the processor;
step five: the processor receives the communication connection instruction and then performs communication connection with the final selection server, and then the processor sends the electric quantity data packet and the state data packet to the final selection server;
Step six: after receiving the electric quantity data packet and the state data packet, the final selection server normalizes the electric quantity data packet according to the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW in the extracted electric quantity data packet, substitutes the electric supplementing time difference BS and the electric supplementing current BL into a model, forms a rectangle by taking the electric supplementing time difference BS and the electric supplementing current BL as length and width respectively, forms a sector by taking lambda times of the square of the difference between the electric supplementing ring temperature BW and 25 ℃ as radius, obtains the shadow part area, and obtains the rough measurement electric quantity CC, wherein lambda is a preset proportion coefficient, and lambda=0.78;
Step seven: the final selection server obtains the time of the electric quantity data packet sent from the processor to the final selection server and the processing time of the electric quantity data packet, marks the time difference FS and the processing time difference CS respectively, substitutes the formula SS=2FS+CS to obtain the transmission time difference SS, obtains the line space value XJ, the line time value XS and the electric production value DC in the state data packet, performs normalization processing on the transmission time difference SS, the line space value XJ, the line time value XS and the electric production value DC, and substitutes the formula Obtaining an adjustment value TJ, wherein d1, d2 and d3 are all preset weight coefficients, d1+d2+d3=1, d1=0.57, d2=0.16 and d3=0.27;
Step eight: the final selection server substitutes the rough measured electric quantity CC and the adjustment value TJ into a formula JC=CC× (1-gamma TJ) to obtain the accurate measured electric quantity JC, and the final selection server sends the accurate measured electric quantity JC to the processor and the database;
step nine: the processor sends the accurate measurement electric quantity JC to the electric quantity display module, and the electric quantity display module displays electric quantity according to the accurate measurement electric quantity JC;
Step ten: and selecting a plurality of accurate measurement electric quantity in the historical record according to the time period by the terminal, taking the time as an X axis, taking the accurate measurement electric quantity as a Y axis, establishing a coordinate system model, and obtaining the time when the accurate measurement electric quantity is reduced to the preset judgment capacity of the battery, thereby obtaining the residual life condition of the battery.
As a further scheme of the invention: a data-driven lithium ion battery residual life prediction system comprises a processor and a final selection server;
the processor is used for generating a selection analysis instruction and a state analysis instruction according to the received electric quantity data packet, sending the selection analysis instruction to the selection analysis module and sending the state analysis instruction to the state analysis module; the processor is also used for receiving the communication connection instruction fed back by the selection analysis module and then carrying out communication connection with the final selection server, and then the processor sends the electric quantity data packet and the state data packet fed back by the state analysis module to the final selection server;
The final selection server is used for obtaining rough measurement electric quantity and transmission time difference according to the electric quantity data packet, obtaining an adjustment value according to the state data packet and the transmission time difference, analyzing the rough measurement electric quantity and the adjustment value to obtain fine measurement electric quantity, and sending the fine measurement electric quantity to the processor and the database, wherein the specific process is as follows:
After receiving the electric quantity data packet and the state data packet, the final selection server normalizes the electric quantity data packet according to the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW in the extracted electric quantity data packet, substitutes the electric supplementing time difference BS and the electric supplementing current BL into a model, forms a rectangle by taking the electric supplementing time difference BS and the electric supplementing current BL as length and width respectively, forms a sector by taking lambda times of the square of the difference between the electric supplementing ring temperature BW and 25 ℃ as radius, obtains the shadow part area, and obtains the rough measurement electric quantity CC, wherein lambda is a preset proportion coefficient, and lambda=0.78;
the final selection server obtains the time of the electric quantity data packet sent from the processor to the final selection server and the processing time of the electric quantity data packet, marks the time difference FS and the processing time difference CS respectively, and substitutes the time difference FS and the processing time difference CS into a formula SS=2FS+CS to obtain a transmission time difference SS;
The final selection server obtains the line space value XJ, the line time value XS and the electric production value DC in the state data packet, normalizes the transmission time difference SS, the line space value XJ, the line time value XS and the electric production value DC, and substitutes the normalized values into a formula Obtaining an adjustment value TJ, wherein d1, d2 and d3 are all preset weight coefficients, d1+d2+d3=1, d1=0.57, d2=0.16 and d3=0.27;
and substituting the rough measured electric quantity CC and the regulating value TJ into a formula JC=CC× (1-gamma TJ) by the final selection server to obtain the accurate measured electric quantity JC, wherein gamma is a preset proportionality coefficient, gamma= 0.0102 is taken, and the final selection server sends the accurate measured electric quantity JC to the processor and the database.
As a further scheme of the invention: the system also comprises an electric quantity acquisition module, wherein the electric quantity acquisition module comprises a voltage monitoring unit, a current acquisition unit and a temperature acquisition unit; the electric quantity acquisition module is used for acquiring battery production time, electricity supplementing time difference, electricity supplementing current and electricity supplementing ring temperature, and sending an electric quantity data packet obtained by analyzing the electricity supplementing time difference, the electricity supplementing current and the electricity supplementing ring temperature to the processor, and sending the battery production time to the state analysis module, wherein the specific process is as follows:
The method comprises the steps that an electric quantity acquisition module acquires battery information of a new energy automobile, acquires charging limit voltage, terminating discharging voltage and battery production time of the battery, monitors time required by the battery to be charged to the charging limit voltage at one time after the battery reaches the terminating discharging voltage through a voltage monitoring unit, marks the time as a power supplementing time difference BS, acquires current in charging in real time through a current acquisition unit, calculates an average value after removing current with the largest value and current with the smallest value, acquires power supplementing current BL, acquires ambient temperature in charging in real time through a temperature acquisition unit, calculates the average value, and acquires power supplementing ring temperature BW, and the electric quantity acquisition module combines the power supplementing time difference BS, the power supplementing current BL and the power supplementing ring temperature BW to form an electric quantity data packet to be sent to a processor, and sends the battery production time to a state analysis module.
As a further scheme of the invention: the system also comprises a selection analysis module, wherein the selection analysis module is used for receiving a selection analysis instruction, obtaining a selection figure of merit XY through analysis, marking a server corresponding to the maximum selection figure of merit XY as a final selection server, generating a communication connection instruction according to the final selection server, and sending the communication connection instruction to a processor, and the specific process is as follows:
The selection analysis module acquires the sending position of the selection analysis instruction after receiving the selection analysis instruction, simultaneously acquires the positions of a plurality of servers, acquires the number of data packets in each server and the total size of all the data packets and marks the number of the data packets SL and the number of the data packets SZ respectively, marks the distance between the positions of the plurality of servers and the sending position of the selection analysis instruction as a time difference SJ in sequence, normalizes the number of the data packets SL, the number of the data packets SZ and the time difference SJ, and substitutes the normalized data packets into a formula And obtaining a selected figure of merit XY, wherein q1, q2 and q3 are all preset weight coefficients, q1+q2+q3=1, taking q1=0.24, q2=0.26 and q3=0.5, marking a server corresponding to the maximum selected figure of merit XY as a final selected server, generating a communication connection instruction according to the final selected server, and sending the communication connection instruction to a processor.
As a further scheme of the invention: the system also comprises a state analysis module, wherein the state analysis module is used for collecting row spacing values, row line values and electric production values after receiving a state analysis instruction, and combining the row spacing values, the row line values and the electric production values to form a state data packet and sending the state data packet to the processor, and the specific process is as follows:
The state analysis module receives a state analysis instruction, acquires the driving distance and the driving time of the new energy automobile, marks the driving distance and the driving time as a line space value XJ and a line space value XS respectively, marks the battery production time as an electric output value DC, and then analyzes the line space value XJ, the line space value XS and the electric output value DC to obtain a state data packet and sends the state data packet to the processor.
The beneficial effects of the invention are as follows:
According to the data-driven lithium ion battery remaining life prediction method and system, the processor is used for sending the electric quantity data packet and the state data packet to the final selection server, the final selection server is used for obtaining the rough electric quantity and the transmission time difference according to the electric quantity data packet, obtaining the adjustment value according to the state data packet and the transmission time difference, correcting the rough electric quantity according to the adjustment value to obtain the precise electric quantity, sending the precise electric quantity to the processor and the database, and finally obtaining the battery remaining life condition, the system only utilizes the processor of the new energy automobile to collect data and conduct simple analysis processing, the main analysis process is conducted through the final selection server, the data processing capacity is improved, meanwhile, the data processing speed is improved, the accuracy of the data processing result is improved, the lithium ion battery remaining life can be accurately predicted, the data processing is conducted in a communication connection mode, and the defects of high detection cost, long detection time and inconvenient geographic position in the traditional method are overcome;
The comprehensive optimal processor can be selected through the selection analysis module to process the data acquired by the new energy automobile, and the server which is used for processing the data acquired by the new energy automobile can be reselected along with the position change of the new energy automobile, so that the accuracy of the residual life of the obtained lithium ion battery is further improved;
the adjustment value is obtained through calculation of the final selection server, the running distance, the running time, the battery production time and the processing time difference of the new energy automobile can be comprehensively considered, so that the situation of the accurate electric quantity of the new energy automobile can be monitored in real time, once the accurate electric quantity is reduced to the preset judgment capacity of the battery, the working personnel can obtain the electric quantity through the terminal, the working personnel can timely make emergency response, and the occurrence of the accident situation of the new energy automobile caused by the service life factor of the battery is reduced.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of a data-driven lithium ion battery remaining life prediction system in accordance with the present invention;
fig. 2 is a schematic diagram of a model established by a final selection server for obtaining rough measurement electric quantity in the invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
Example 1:
referring to fig. 1-2, the present embodiment is a data-driven lithium ion battery remaining life prediction system, including a processor and a final selection server;
The processor is used for generating a selection analysis instruction and a state analysis instruction according to the received electric quantity data packet, sending the selection analysis instruction to the selection analysis module and sending the state analysis instruction to the state analysis module; the processor is also used for receiving the communication connection instruction fed back by the selection analysis module and then carrying out communication connection with the final selection server, and then the processor sends the electric quantity data packet and the state data packet fed back by the state analysis module to the final selection server;
The final selection server is used for obtaining rough measurement electric quantity and transmission time difference according to the electric quantity data packet, obtaining an adjustment value according to the state data packet, analyzing the rough measurement electric quantity and the adjustment value to obtain fine measurement electric quantity, and sending the fine measurement electric quantity to the processor and the database, wherein the specific process is as follows:
After receiving the electric quantity data packet and the state data packet, the final selection server normalizes the electric quantity data packet according to the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW in the extracted electric quantity data packet, substitutes the electric supplementing time difference BS and the electric supplementing current BL into a model, respectively takes the electric supplementing time difference BS and the electric supplementing current BL as length and width to form a rectangle, takes lambda times of the square of the difference between the electric supplementing ring temperature BW and 25 ℃ as radius to form a sector, calculates the area of a shadow part, and obtains the rough measurement electric quantity CC, wherein lambda is a preset proportion coefficient, and lambda=0.78;
the final selection server obtains the time of the electric quantity data packet sent from the processor to the final selection server and the processing time of the electric quantity data packet, marks the time difference FS and the processing time difference CS respectively, and substitutes the time difference FS and the processing time difference CS into a formula SS=2FS+CS to obtain a transmission time difference SS;
The final selection server obtains the line space value XJ, the line time value XS and the electric production value DC in the state data packet, normalizes the transmission time difference SS, the line space value XJ, the line time value XS and the electric production value DC, and substitutes the normalized values into a formula Obtaining an adjustment value TJ, wherein d1, d2 and d3 are all preset weight coefficients, d1+d2+d3=1, d1=0.57, d2=0.16 and d3=0.27;
and substituting the rough measured electric quantity CC and the regulating value TJ into a formula JC=CC× (1-gamma TJ) by the final selection server to obtain the accurate measured electric quantity JC, wherein gamma is a preset proportionality coefficient, gamma= 0.0102 is taken, and the final selection server sends the accurate measured electric quantity JC to the processor and the database.
Example 2:
Referring to fig. 1-2, the present embodiment is a data-driven lithium ion battery remaining life prediction system, which further includes an electric quantity acquisition module, a selection analysis module, and a state analysis module;
The electric quantity acquisition module is used for acquiring battery production time, electricity supplementing time difference, electricity supplementing current and electricity supplementing ring temperature, and sending an electric quantity data packet obtained by analyzing the electricity supplementing time difference, the electricity supplementing current and the electricity supplementing ring temperature to the processor, and sending the battery production time to the state analysis module, wherein the specific process is as follows:
The method comprises the steps that an electric quantity acquisition module acquires battery information of a new energy automobile, acquires charging limit voltage, terminating discharging voltage and battery production time of the battery, monitors time required by the battery to reach the charging limit voltage after the battery reaches the terminating discharging voltage through a voltage monitoring unit, marks the time as an electric supplementing time difference BS, acquires current in charging in real time through a current acquisition unit, calculates an average value after removing current with the largest value and current with the smallest value, acquires electric supplementing current BL, acquires environmental temperature in charging in real time through a temperature acquisition unit, calculates the average value, and acquires electric supplementing ring temperature BW, and the electric quantity acquisition module combines the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW to form an electric quantity data packet to be sent to a processor, and sends the battery production time to a state analysis module;
The selection analysis module is used for receiving the selection analysis instruction, obtaining a selected figure of merit XY through analysis, marking a server corresponding to the maximum selected figure of merit XY as a final selected server, generating a communication connection instruction according to the final selected server, and sending the communication connection instruction to the processor, wherein the specific process is as follows:
The selection analysis module acquires the sending position of the selection analysis instruction after receiving the selection analysis instruction, simultaneously acquires the positions of a plurality of servers, acquires the number of data packets in each server and the total size of all the data packets and marks the number of the data packets SL and the number of the data packets SZ respectively, marks the distance between the positions of the plurality of servers and the sending position of the selection analysis instruction as a time difference SJ in sequence, normalizes the number of the data packets SL, the number of the data packets SZ and the time difference SJ, and substitutes the normalized data packets into a formula Obtaining a selected figure of merit XY, wherein q1, q2 and q3 are all preset weight coefficients, q1+q2+q3=1, taking q1=0.24, q2=0.26 and q3=0.5, marking a server corresponding to the maximum selected figure of merit XY as a final selected server, generating a communication connection instruction according to the final selected server, and sending the communication connection instruction to a processor;
The state analysis module is used for collecting the row spacing value, the line time value and the electric output value after receiving the state analysis instruction, and combining the row spacing value, the line time value and the electric output value to form a state data packet and sending the state data packet to the processor, and the specific process is as follows:
The state analysis module receives a state analysis instruction, acquires the driving distance and the driving time of the new energy automobile, marks the driving distance and the driving time as a line space value XJ and a line space value XS respectively, marks the battery production time as an electric output value DC, and then analyzes the line space value XJ, the line space value XS and the electric output value DC to obtain a state data packet and sends the state data packet to the processor.
Example 3:
Referring to fig. 1-2, in combination with embodiment 1 and embodiment 2, the present embodiment is a data-driven method for predicting remaining life of a lithium ion battery, comprising the following steps:
Step one: the method comprises the steps that an electric quantity acquisition module acquires battery information of a new energy automobile, acquires charging limit voltage, terminating discharging voltage and battery production time of the battery, monitors time required by the battery to reach the charging limit voltage after the battery reaches the terminating discharging voltage through a voltage monitoring unit, marks the time as an electric supplementing time difference BS, acquires current in charging in real time through a current acquisition unit, calculates an average value after removing current with the largest value and current with the smallest value, acquires electric supplementing current BL, acquires environmental temperature in charging in real time through a temperature acquisition unit, calculates the average value, and acquires electric supplementing ring temperature BW, and the electric quantity acquisition module combines the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW to form an electric quantity data packet to be sent to a processor, and sends the battery production time to a state analysis module;
step two: the processor receives the electric quantity data packet, generates a selection analysis instruction and a state analysis instruction, sends the selection analysis instruction to the selection analysis module, and sends the state analysis instruction to the state analysis module;
Step three: the selection analysis module acquires the sending position of the selection analysis instruction after receiving the selection analysis instruction, simultaneously acquires the positions of a plurality of servers, acquires the number of data packets in each server and the total size of all the data packets and marks the number of the data packets SL and the number of the data packets SZ respectively, marks the distance between the positions of the plurality of servers and the sending position of the selection analysis instruction as a time difference SJ in sequence, normalizes the number of the data packets SL, the number of the data packets SZ and the time difference SJ, and substitutes the normalized data packets into a formula Obtaining a selected figure of merit XY, wherein q1, q2 and q3 are all preset weight coefficients, q1+q2+q3=1, taking q1=0.24, q2=0.26 and q3=0.5, marking a server corresponding to the maximum selected figure of merit XY as a final selected server, generating a communication connection instruction according to the final selected server, and sending the communication connection instruction to a processor;
Step four: the state analysis module receives a state analysis instruction, acquires the driving distance and the driving time of the new energy automobile, marks the driving distance and the driving time as a line space value XJ and a line space value XS respectively, marks the battery production time as an electric output value DC, and then combines the line space value XJ, the line space value XS and the electric output value DC to form a state data packet and sends the state data packet to the processor;
step five: the processor receives the communication connection instruction and then performs communication connection with the final selection server, and then the processor sends the electric quantity data packet and the state data packet to the final selection server;
Step six: after receiving the electric quantity data packet and the state data packet, the final selection server normalizes the electric quantity data packet according to the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW in the extracted electric quantity data packet, substitutes the electric supplementing time difference BS and the electric supplementing current BL into a model, respectively takes the electric supplementing time difference BS and the electric supplementing current BL as length and width to form a rectangle, takes lambda times of the square of the difference between the electric supplementing ring temperature BW and 25 ℃ as radius to form a sector, calculates the area of a shadow part, and obtains the rough measurement electric quantity CC, wherein lambda is a preset proportion coefficient, and lambda=0.78;
Step seven: the final selection server obtains the time of the electric quantity data packet sent from the processor to the final selection server and the processing time of the electric quantity data packet, marks the time difference FS and the processing time difference CS respectively, substitutes the formula SS=2FS+CS to obtain the transmission time difference SS, obtains the line space value XJ, the line time value XS and the electric production value DC in the state data packet, performs normalization processing on the transmission time difference SS, the line space value XJ, the line time value XS and the electric production value DC, and substitutes the formula Obtaining an adjustment value TJ, wherein d1, d2 and d3 are all preset weight coefficients, d1+d2+d3=1, d1=0.57, d2=0.16 and d3=0.27;
Step eight: the final selection server substitutes the rough measured electric quantity CC and the adjustment value TJ into a formula JC=CC× (1-gamma TJ) to obtain the accurate measured electric quantity JC, and the final selection server sends the accurate measured electric quantity JC to the processor and the database;
step nine: the processor sends the accurate measurement electric quantity JC to the electric quantity display module, and the electric quantity display module displays electric quantity according to the accurate measurement electric quantity JC;
Step ten: and selecting a plurality of accurate measurement electric quantity in the historical record according to the time period by the terminal, taking the time as an X axis, taking the accurate measurement electric quantity as a Y axis, establishing a coordinate system model, and obtaining the time when the accurate measurement electric quantity is reduced to the preset judgment capacity of the battery, thereby obtaining the residual life condition of the battery.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
Claims (5)
1. The data-driven lithium ion battery remaining life prediction method is characterized by comprising the following steps of:
Step one: the method comprises the steps that an electric quantity acquisition module acquires battery information of a new energy automobile, acquires charging limit voltage, terminating discharging voltage and battery production time of the battery, monitors time required by the battery to reach the charging limit voltage after the battery reaches the terminating discharging voltage through a voltage monitoring unit, marks the time as an electric supplementing time difference BS, acquires current in charging in real time through a current acquisition unit, calculates an average value after removing current with the largest value and current with the smallest value, acquires electric supplementing current BL, acquires environmental temperature in charging in real time through a temperature acquisition unit, calculates the average value, and acquires electric supplementing ring temperature BW, and the electric quantity acquisition module combines the electric supplementing time difference BS, the electric supplementing current BL and the electric supplementing ring temperature BW to form an electric quantity data packet to be sent to a processor, and sends the battery production time to a state analysis module;
step two: the processor receives the electric quantity data packet, generates a selection analysis instruction and a state analysis instruction, sends the selection analysis instruction to the selection analysis module, and sends the state analysis instruction to the state analysis module;
Step three: the method comprises the steps that after a selection analysis module receives a selection analysis instruction, a sending position of the selection analysis instruction is obtained, the selection analysis module simultaneously collects the positions of a plurality of servers, the number of data packets in each server and the total size of all the data packets are obtained and marked as a data packet quantity SL and a data packet SZ respectively, the selection analysis module marks the distances between the positions of the plurality of servers and the sending position of the selection analysis instruction as time differences SJ in sequence, the selection analysis module analyzes the data packet quantity SL, the data packet value SZ and the time differences SJ to obtain a selection value XY, marks a server corresponding to the maximum selection value XY as a final selection server, generates a communication connection instruction according to the final selection server, and sends the communication connection instruction to a processor;
Step four: the state analysis module receives a state analysis instruction, acquires the driving distance and the driving time of the new energy automobile, marks the driving distance and the driving time as a line space value XJ and a line space value XS respectively, marks the battery production time as an electric output value DC, and then combines the line space value XJ, the line space value XS and the electric output value DC to form a state data packet and sends the state data packet to the processor;
step five: the processor receives the communication connection instruction and then performs communication connection with the final selection server, and then the processor sends the electric quantity data packet and the state data packet to the final selection server;
Step six: after receiving the electric quantity data packet and the state data packet, the final selection server analyzes the electric quantity data packet according to the supplementary electricity time difference BS, the supplementary electricity current BL and the supplementary electricity ring temperature BW in the extracted electric quantity data packet to obtain a rough measurement electric quantity CC;
Step seven: the final selection server obtains the time of the electric quantity data packet sent from the processor to the final selection server and the processing time of the electric quantity data packet, marks the time of the electric quantity data packet as a sending time difference FS and a processing time difference CS respectively, obtains a transmission time difference SS through analysis, obtains a line spacing value XJ, a line duration value XS and an electric output value DC in the state data packet, and obtains an adjustment value TJ through analysis of the transmission time difference SS, the line spacing value XJ, the line duration value XS and the electric output value DC;
Step eight: the final selection server substitutes the rough measured electric quantity CC and the adjustment value TJ into a formula, and the rough measured electric quantity CC and the adjustment value TJ are analyzed to obtain the accurate measured electric quantity JC, and the final selection server sends the accurate measured electric quantity JC to the processor and the database;
step nine: the processor sends the accurate measurement electric quantity JC to the electric quantity display module, and the electric quantity display module displays electric quantity according to the accurate measurement electric quantity JC;
Step ten: and selecting a plurality of accurate measurement electric quantity in the historical record according to the time period by the terminal, taking the time as an X axis, taking the accurate measurement electric quantity as a Y axis, establishing a coordinate system model, and obtaining the time when the accurate measurement electric quantity is reduced to the preset judgment capacity of the battery, thereby obtaining the residual life condition of the battery.
2. The data-driven lithium ion battery residual life prediction system is characterized by comprising a processor and a final selection server;
the processor is used for generating a selection analysis instruction and a state analysis instruction according to the received electric quantity data packet, sending the selection analysis instruction to the selection analysis module and sending the state analysis instruction to the state analysis module; the processor is also used for receiving the communication connection instruction fed back by the selection analysis module and then carrying out communication connection with the final selection server, and then the processor sends the electric quantity data packet and the state data packet fed back by the state analysis module to the final selection server;
The final selection server is used for obtaining rough measurement electric quantity and transmission time difference according to the electric quantity data packet, obtaining an adjustment value according to the state data packet and the transmission time difference, analyzing the rough measurement electric quantity and the adjustment value to obtain fine measurement electric quantity, and sending the fine measurement electric quantity to the processor and the database, wherein the specific process is as follows:
After receiving the electric quantity data packet and the state data packet, the final selection server respectively forms a rectangle with the length and the width of the electric quantity time difference BS and the electric quantity current BL according to the electric quantity time difference BS, the electric quantity current BL and the electric quantity loop temperature BW in the extracted electric quantity data packet, forms a sector with lambda times of the square of the difference between the electric quantity loop temperature BW and 25 ℃ as a radius, and obtains the shadow area to obtain the rough measurement electric quantity CC, wherein lambda is a preset proportionality coefficient;
The final selection server obtains the time of the electric quantity data packet sent from the processor to the final selection server and the processing time of the electric quantity data packet and marks the time as a sending time difference FS and a processing time difference CS respectively, and the transmission time difference SS is obtained through analysis;
The final selection server obtains a row spacing value XJ, a row time value XS and an electric production value DC in the state data packet, and analyzes the transmission time difference SS, the row spacing value XJ, the row time value XS and the electric production value DC to obtain a regulating value TJ;
and the final selection server analyzes the rough measured electric quantity CC and the regulating value TJ to obtain the accurate measured electric quantity JC, and the final selection server sends the accurate measured electric quantity JC to the processor and the database.
3. The system for predicting the remaining life of a data-driven lithium ion battery according to claim 2, further comprising an electric quantity acquisition module, wherein the electric quantity acquisition module is used for acquiring battery production time, power supply time difference, power supply current and power supply ring temperature, and sending an electric quantity data packet obtained by analyzing the power supply time difference, the power supply current and the power supply ring temperature to the processor, and sending the battery production time to the state analysis module, and the specific process is as follows:
The method comprises the steps that an electric quantity acquisition module acquires battery information of a new energy automobile, acquires charging limit voltage, terminating discharging voltage and battery production time of the battery, monitors time required by the battery to be charged to the charging limit voltage at one time after the battery reaches the terminating discharging voltage through a voltage monitoring unit, marks the time as a power supplementing time difference BS, acquires current in charging in real time through a current acquisition unit, calculates an average value after removing current with the largest value and current with the smallest value, acquires power supplementing current BL, acquires ambient temperature in charging in real time through a temperature acquisition unit, calculates the average value, and acquires power supplementing ring temperature BW, and the electric quantity acquisition module combines the power supplementing time difference BS, the power supplementing current BL and the power supplementing ring temperature BW to form an electric quantity data packet to be sent to a processor, and sends the battery production time to a state analysis module.
4. The system for predicting remaining life of a data-driven lithium ion battery according to claim 3, further comprising a selection analysis module, wherein the selection analysis module is configured to receive a selection analysis instruction, obtain a selection value XY through analysis, mark a server corresponding to a maximum selection value XY as a final selection server, generate a communication connection instruction according to the final selection server, and send the communication connection instruction to the processor, and the specific process is as follows:
the selection analysis module acquires the sending position of the selection analysis instruction after receiving the selection analysis instruction, simultaneously acquires the positions of a plurality of servers, acquires the number of data packets in each server and the total size of all the data packets, marks the number of data packets SL and the number of data packets SZ respectively, marks the distances between the positions of the plurality of servers and the sending position of the selection analysis instruction as time differences SJ in sequence, analyzes the number of data packets SL, the number of data packets SZ and the time differences SJ to obtain a selection value XY, marks the server corresponding to the maximum selection value XY as a final selection server, generates a communication connection instruction according to the final selection server, and sends the communication connection instruction to the processor.
5. The system of claim 4, further comprising a state analysis module, wherein the state analysis module is configured to collect a row spacing value, a row time value, and an electrical yield value after receiving a state analysis command, and combine the row spacing value, the row time value, and the electrical yield value to form a state data packet, and send the state data packet to the processor, wherein the state data packet is specifically as follows:
The state analysis module receives a state analysis instruction, acquires the driving distance and the driving time of the new energy automobile, marks the driving distance and the driving time as a line space value XJ and a line space value XS respectively, marks the battery production time as an electric output value DC, and then analyzes the line space value XJ, the line space value XS and the electric output value DC to obtain a state data packet and sends the state data packet to the processor.
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| CN110208705A (en) * | 2019-05-09 | 2019-09-06 | 赛尔网络有限公司 | A kind of lithium battery method for predicting residual useful life and device |
| CN112115404A (en) * | 2020-09-08 | 2020-12-22 | 中国第一汽车股份有限公司 | Method, device, system, equipment and storage medium for pre-judging electric quantity of vehicle battery |
| EP3845918A1 (en) * | 2020-01-06 | 2021-07-07 | Tata Consultancy Services Limited | Method and system for online estimation of soh and rul of a battery |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110208705A (en) * | 2019-05-09 | 2019-09-06 | 赛尔网络有限公司 | A kind of lithium battery method for predicting residual useful life and device |
| EP3845918A1 (en) * | 2020-01-06 | 2021-07-07 | Tata Consultancy Services Limited | Method and system for online estimation of soh and rul of a battery |
| CN112115404A (en) * | 2020-09-08 | 2020-12-22 | 中国第一汽车股份有限公司 | Method, device, system, equipment and storage medium for pre-judging electric quantity of vehicle battery |
Non-Patent Citations (1)
| Title |
|---|
| 基于数据驱动的卫星锂离子电池寿命预测方法;艾力;房红征;于功敬;樊焕贞;;计算机测量与控制;20150425(04);全文 * |
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