CN116080470A - Power battery monitoring method and device for electric automobile, server and medium - Google Patents

Power battery monitoring method and device for electric automobile, server and medium Download PDF

Info

Publication number
CN116080470A
CN116080470A CN202211338422.0A CN202211338422A CN116080470A CN 116080470 A CN116080470 A CN 116080470A CN 202211338422 A CN202211338422 A CN 202211338422A CN 116080470 A CN116080470 A CN 116080470A
Authority
CN
China
Prior art keywords
historical
battery
charging
attenuation
discharging
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211338422.0A
Other languages
Chinese (zh)
Inventor
魏正桥
龙美元
郝金隆
戴娇
李光祝
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing Changan Automobile Co Ltd
Original Assignee
Chongqing Changan Automobile Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing Changan Automobile Co Ltd filed Critical Chongqing Changan Automobile Co Ltd
Priority to CN202211338422.0A priority Critical patent/CN116080470A/en
Publication of CN116080470A publication Critical patent/CN116080470A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Sustainable Energy (AREA)
  • Transportation (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Sustainable Development (AREA)
  • Computer Hardware Design (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Human Computer Interaction (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Geometry (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The application relates to the technical field of batteries of electric vehicles, in particular to a power battery monitoring method, a device, a server and a medium of an electric vehicle, wherein the method comprises the following steps: receiving charging data and discharging data acquired by a target vehicle in a preset state of charge (SOC) interval; according to the identity of the target vehicle, matching a battery attenuation model obtained by training in advance, inputting charging data and discharging data into the battery attenuation model, and outputting the actual attenuation degree of a power battery in the target vehicle; if the actual attenuation degree is smaller than the preset abnormal threshold value, the power battery is judged to be in a preset abnormal state, and an early warning prompt is generated for the power battery of the target vehicle, otherwise, the power battery of the target vehicle is judged to be in a preset normal state. Therefore, the problems that a user cannot timely and accurately master the attenuation condition of the battery, the efficiency is extremely low, the time and the cost are wasted, the safety of the user in use is reduced, the use experience of the user is reduced and the like in the related technology are solved.

Description

Power battery monitoring method and device for electric automobile, server and medium
Technical Field
The application relates to the technical field of batteries of electric vehicles, in particular to a power battery monitoring method, a device, a server and a medium of an electric vehicle.
Background
Along with the rapid development of new energy electric vehicles, the electric vehicle battery is not only a core part of the electric vehicle, but also a key technical bottleneck for restricting the development of the electric vehicle, and in the use process of the electric vehicle, the power battery can be attenuated along with the increase of the number of times of cyclic charge and discharge, the rated electric quantity of the battery can be reduced, meanwhile, the attenuation of the power battery can cause wrong judgment of the endurance mileage, and serious instability of the whole vehicle current and voltage can occur, so that the normal use of the vehicle is affected.
In the related art, an electric automobile user cannot know the attenuation condition of a battery in real time, can only detect through professional equipment of a manufacturer, then decides to replace the battery according to the specific attenuation condition of the battery, has extremely low efficiency and wastes time and cost, cannot fundamentally avoid traffic accidents caused by battery faults of vehicles, reduces the safety of the user in use and reduces the use experience of the user.
Disclosure of Invention
The application provides a power battery monitoring method, device, server and medium for an electric automobile, which are used for solving the problems that a user cannot timely and accurately master the battery attenuation condition in the related technology, the efficiency is extremely low, the time and the cost are wasted, the safety of the user in use is reduced, the use experience of the user is reduced and the like.
An embodiment of a first aspect of the present application provides a method for monitoring a power battery of an electric vehicle, where the method is applied to a server, and includes the following steps: receiving charging data and discharging data collected by a target vehicle in a preset State of Charge (SOC) interval; according to the battery attenuation model obtained by the pre-training of the identity identification matching of the target vehicle, the charging data and the discharging data are input into the battery attenuation model, and the actual attenuation degree of the power battery in the target vehicle is output; if the actual attenuation degree is smaller than a preset abnormal threshold value, judging that the power battery is in a preset abnormal state, generating a power battery of the target vehicle to perform early warning prompt, otherwise, judging that the power battery of the target vehicle is in a preset normal state.
According to the technical means, according to the embodiment of the application, the related data of the target vehicle in the charge and discharge state is collected, the attenuation degree model obtained through training in advance is matched according to the identity of the target vehicle, the data is input into the battery attenuation model, the actual attenuation degree of the power battery is calculated in real time through the battery attenuation model, and when the actual attenuation value is within the normal threshold value, the power battery of the vehicle is judged to be in the normal state; when the actual attenuation degree is smaller than the abnormal threshold value, the battery is judged to be abnormal and early warning prompt is carried out, so that real-time calculation and monitoring of the attenuation cloud of the power battery of the electric automobile are realized, a user can timely and accurately grasp the attenuation state of the battery, potential safety hazards caused by attenuation of the battery are avoided, the detection time and maintenance cost are reduced, and the use safety of the electric automobile of the customer is improved.
Optionally, the charging data includes a charging voltage, a charging current and a charging time, the discharging data includes a discharging voltage, a discharging current and a discharging time, the charging data and the discharging data are input into the battery attenuation model, and an actual attenuation degree of a power battery in any vehicle is output, including: calculating actual charging power according to the charging voltage, the charging current and the charging time; calculating actual discharge power according to the discharge voltage, the discharge current and the discharge time; and calculating the ratio of the actual charging power to the actual discharging power, and calculating the actual attenuation degree of the power battery according to the ratio.
According to the technical means, according to the embodiment of the application, the actual charging power is calculated according to the charged related data, the actual discharging power is calculated according to the discharged related data, then the battery attenuation degree is calculated according to the ratio of the time charging power to the actual discharging power, the battery attenuation degree is calculated according to the ratio of the discharging power to the charging power by fully considering the attenuation degree, and the actual situation is more fitted, so that the obtained data has more practical value and is more accurate.
Optionally, the charging data further includes a battery charging temperature, a first ambient temperature, and a number of times of charging, the discharging data further includes a battery discharging temperature, a second ambient temperature, and a number of times of discharging, and calculating the actual attenuation degree of the power battery according to the ratio includes: matching a damping degree correction coefficient according to the battery charging temperature, the first ambient temperature, the charging times, the battery discharging temperature, the second ambient temperature and/or the discharging times; and calculating the actual attenuation degree of the power battery according to the ratio and the attenuation degree correction coefficient.
According to the technical means, the embodiment of the application calculates the matching attenuation degree correction coefficient through external factors such as the charging temperature, the charging frequency, the ambient temperature, the discharging temperature of the battery and the like of the battery, calculates the actual attenuation degree of the power battery according to the ratio of the time charging power to the actual discharging power and the attenuation degree correction coefficient, fully considers the influence of the external factors on the actual attenuation degree of the power battery, and makes relevant correction in calculation, thereby ensuring the accuracy of calculation.
Optionally, the battery decay model is trained based on historical charge data and historical discharge data of the target vehicle, including: acquiring historical charging data and historical discharging data of the target vehicle in a preset SOC interval, wherein the historical charging data comprises historical charging voltage, historical charging current and historical charging time, and the historical discharging data comprises historical discharging voltage, historical discharging current and historical discharging time; calculating one or more historical charging powers according to the historical charging voltage, the historical charging current and the historical charging time; calculating one or more historical discharge powers according to the historical discharge voltage, the historical discharge current and the historical discharge time; calculating the ratio of the historical charging power to the historical discharging power, calculating one or more attenuation degrees of the power battery by using the ratio, and training a neural network by using the one or more attenuation degrees to obtain the battery attenuation model.
According to the technical means, the embodiment of the application obtains the historical charge data and the historical discharge data of the target vehicle in the SOC interval, calculates the historical charge and discharge power according to the historical charge and discharge voltage, the historical charge and discharge current and the historical charge and discharge time in the historical data, calculates the ratio of the historical charge power to the historical discharge power to obtain a plurality of historical attenuation degrees, trains the neural network through the attenuation degrees to obtain a battery attenuation model, continuously optimizes the battery attenuation degree model, and finally obtains the battery attenuation degree with higher accuracy.
Optionally, the historical charging data further includes a historical battery charging temperature, a historical first ambient temperature and a historical charging frequency, the historical discharging data further includes a historical battery discharging temperature, a historical second ambient temperature and a historical discharging frequency, the calculating by using the ratio obtains one or more attenuation degrees of the power battery, and the method includes: calculating according to the historical battery charging temperature, the historical first environment temperature, the historical charging times, the historical battery discharging temperature, the historical second environment temperature and/or the historical discharging times to obtain a damping degree correction coefficient; and calculating one or more attenuation degrees of the power battery by using the attenuation degree correction coefficient and the ratio.
According to the technical means, the embodiment of the application obtains the historical attenuation degree correction coefficient according to the historical related battery charging temperature, the environmental temperature, the discharging temperature and the like, obtains a plurality of attenuation degrees of the power battery according to the historical attenuation degree correction coefficient and the ratio of the historical charging power to the historical discharging power, and obtains the battery attenuation degree with higher accuracy by continuously optimizing the battery attenuation degree model in consideration of the influence of external factors on the actual attenuation degree of the power battery.
Optionally, after outputting the actual attenuation degree of the power battery in the target vehicle, the method further includes: and optimizing the battery attenuation model by using the actual attenuation degree.
It can be understood that the embodiment of the application optimizes the battery attenuation model by using the actual attenuation degree, so that the calculation result is more accurate, and a user can conveniently and accurately grasp the battery attenuation condition.
An embodiment of a second aspect of the present application provides a power battery monitoring device for an electric vehicle, where the device is applied to a server, and includes: the receiving module is used for receiving charging data and discharging data acquired by the target vehicle in a preset state of charge (SOC) interval; the processing module is used for matching a battery attenuation model obtained through training in advance according to the identity of the target vehicle, inputting the charging data and the discharging data into the battery attenuation model, and outputting the actual attenuation degree of the power battery in the target vehicle; and the judging module is used for judging that the power battery is in a preset abnormal state if the actual attenuation degree is smaller than a preset abnormal threshold value, generating the power battery of the target vehicle for early warning prompt, and otherwise judging that the power battery of the target vehicle is in a preset normal state.
Optionally, the processing module is configured to: calculating actual charging power according to the charging voltage, the charging current and the charging time; calculating actual discharge power according to the discharge voltage, the discharge current and the discharge time; and calculating the ratio of the actual charging power to the actual discharging power, and calculating the actual attenuation degree of the power battery according to the ratio.
Optionally, the processing module is further configured to: matching a damping degree correction coefficient according to the battery charging temperature, the first environment temperature, the charging times, the battery discharging temperature, the second environment temperature and/or the discharging times; and calculating the actual attenuation degree of the power battery according to the ratio and the attenuation degree correction coefficient.
Optionally, the processing module is further configured to: acquiring historical charging data and historical discharging data of the target vehicle in a preset SOC interval, wherein the historical charging data comprises historical charging voltage, historical charging current and historical charging time, and the historical discharging data comprises historical discharging voltage, historical discharging current and historical discharging time; calculating one or more historical charging powers according to the historical charging voltage, the historical charging current and the historical charging time; calculating one or more historical discharge powers according to the historical discharge voltage, the historical discharge current and the historical discharge time; calculating the ratio of the historical charging power to the historical discharging power, calculating one or more attenuation degrees of the power battery by using the ratio, and training a neural network by using the one or more attenuation degrees to obtain the battery attenuation model.
Optionally, the processing module is further configured to: calculating according to the historical battery charging temperature, the historical first environment temperature, the historical charging times, the historical battery discharging temperature, the historical second environment temperature and/or the historical discharging times to obtain a damping degree correction coefficient; and calculating one or more attenuation degrees of the power battery by using the attenuation degree correction coefficient and the ratio.
Optionally, the processing module is further configured to: and optimizing the battery attenuation model by using the actual attenuation degree.
An embodiment of a third aspect of the present application provides a server, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the power battery monitoring method of the electric automobile according to the embodiment.
An embodiment of a fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program that is executed by a processor for implementing the power battery monitoring method of an electric vehicle as described in the above embodiment.
Therefore, the application has at least the following beneficial effects:
(1) According to the embodiment of the application, the related data of the target vehicle in the charge and discharge state is collected, the attenuation degree model obtained through training in advance is matched according to the identity of the target vehicle, the data is input into the battery attenuation model, the actual attenuation degree of the power battery is calculated in real time through the battery attenuation model, and when the actual attenuation value is within a normal threshold value, the power battery of the vehicle is judged to be in a normal state; when the actual attenuation degree is smaller than the abnormal threshold value, the battery is judged to be abnormal and early warning prompt is carried out, so that real-time calculation and monitoring of the attenuation cloud of the power battery of the electric automobile are realized, a user can timely and accurately grasp the attenuation state of the battery, potential safety hazards caused by attenuation of the battery are avoided, the detection time and maintenance cost are reduced, and the use safety of the electric automobile of the customer is improved.
(2) According to the embodiment of the application, the actual charging power is calculated according to the charged related data, the actual discharging power is calculated according to the discharged related data, then the battery attenuation degree is calculated according to the ratio of the time charging power to the actual discharging power, the battery attenuation degree is calculated according to the ratio of the discharging power to the charging power, the actual situation is more attached, the obtained data has more practical value, and the obtained data is more accurate.
(3) According to the embodiment of the application, the external factors such as the charging temperature, the charging frequency, the ambient temperature and the discharging temperature of the battery are used for calculating the matching attenuation degree correction coefficient, the actual attenuation degree of the power battery is calculated according to the ratio of the time charging power to the actual discharging power and the attenuation degree correction coefficient, the influence of the external factors on the actual attenuation degree of the power battery is fully considered, relevant correction is carried out in calculation, and the accuracy of calculation is guaranteed.
(4) According to the method and the device for obtaining the battery attenuation degree, historical charging data and historical discharging data of the target vehicle in the SOC interval are obtained, historical charging and discharging power is calculated according to historical charging and discharging voltage, historical charging and discharging current and historical charging and discharging time in the historical data, the ratio of the historical charging power to the historical discharging power is calculated to obtain a plurality of historical attenuation degrees, a neural network is trained through the attenuation degrees to obtain a battery attenuation model, the battery attenuation degree model is optimized continuously, and finally the battery attenuation degree with higher accuracy is obtained.
(5) According to the embodiment of the application, the historical attenuation degree correction coefficient is obtained according to the historical related battery charging temperature, the environmental temperature, the discharging temperature and the like, a plurality of attenuation degrees of the power battery are obtained according to the historical attenuation degree correction coefficient and the ratio of the historical charging power to the historical discharging power, the influence of external factors on the actual attenuation degree of the power battery is considered, and the battery attenuation degree with higher accuracy is obtained through continuous optimization of a battery attenuation degree model.
(6) According to the embodiment of the application, the actual attenuation degree is utilized to optimize the battery attenuation model, so that the calculation result is more accurate, and a user can conveniently and accurately grasp the battery attenuation condition.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a power battery monitoring method of an electric vehicle according to an embodiment of the present application;
FIG. 2 is an overall flow chart for real-time calculation and monitoring of the attenuation of an automotive battery according to an embodiment of the present application;
FIG. 3 is a flow chart of modeling vehicle battery degradation according to an embodiment of the present application;
FIG. 4 is a flow chart of real-time calculation of the attenuation of an automotive battery according to an embodiment of the application;
FIG. 5 is a diagram of an overall technique for modeling and computing the attenuation of an automotive battery according to an embodiment of the present application;
fig. 6 is a block schematic diagram of a power battery monitoring device of an electric vehicle according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
With the rapid development of new energy electric vehicles, an electric vehicle battery is not only a core part of the electric vehicle, but also a key technical bottleneck for restricting the development of the electric vehicle, and a power battery can generate battery attenuation along with the increase of the number of times of cyclic charge and discharge, the rated electric quantity of the battery can be reduced, meanwhile, the power battery attenuation leads to misjudgment of the endurance mileage, serious and even unstable current and voltage of the whole vehicle can occur, the normal use of the vehicle is influenced, the driving safety is endangered, the real-time calculation and the monitoring of the power battery attenuation of the electric vehicle are established, an automobile manufacturer and a user can be helped to timely and accurately master the battery attenuation condition, a basis is provided for the manufacturer to provide more intelligent and more efficient battery maintenance and maintenance for the user, potential safety hazards caused by the battery attenuation are avoided, and the safety of the client vehicle is improved.
In the related technology (1), the battery attenuation is calculated by only considering the ratio of the electric quantity charged by the target battery to the rated battery electric quantity, the electric quantity stored by the actual battery is not considered, the calculated result deviation is larger, the influence of the environment temperature, the battery temperature and the battery cycle charge and discharge times on the battery charge and discharge and the battery attenuation is ignored, the acquired data cannot calculate and monitor the corresponding vehicle in real time, and the judgment of the attenuation of the whole vehicle battery is further influenced.
In the related art (2), only the collection of charging data through a charging condition is considered, the input electric quantity of the battery is calculated through a formula q= I.T, and because the current of the battery is changed in the charging process, an author predicts the electric quantity in a mode of extracting a characteristic value through an SVM model, and determines the health state of the battery by predicting the ratio of the charge-in electric quantity to the initial capacity of the battery under the charging condition, and the charge-in electric quantity is also only considered, but the discharge electric quantity is not considered.
Therefore, the embodiment of the application provides a method, a device, a server and a medium for monitoring a power battery of an electric automobile, wherein the embodiment of the application calculates the attenuation degree of the battery through the ratio of the actual discharging electric quantity to the actual charging electric quantity, improves the accuracy of monitoring, and is described below with reference to the accompanying drawings.
Specifically, fig. 1 is a schematic flow chart of a power battery monitoring method of an electric vehicle according to an embodiment of the present application.
As shown in fig. 1, the method for monitoring the power battery of the electric automobile comprises the following steps:
in step S101, charge data and discharge data collected by the target vehicle in a preset state of charge SOC interval are received.
The preset state of charge may be a ratio of a remaining capacity of the lithium battery after the lithium battery is used for a period of time or is left unused for a long period of time to a capacity of the lithium battery in a fully charged state, and is generally expressed by a percentage, namely, a binary hexadecimal system, which means that the remaining capacity is 0% -100%, the soc=0 indicates that the battery is completely discharged, and the soc=1 indicates that the battery is completely full, which is not limited herein.
It can be appreciated that the embodiment of the application prepares for the subsequent matching of the battery attenuation model by receiving the charge data and the discharge data acquired by the target vehicle in the SOC interval.
In step S102, the battery attenuation model obtained by training in advance is matched according to the identity of the target vehicle, the charging data and the discharging data are input into the battery attenuation model, and the actual attenuation degree of the power battery in the target vehicle is output.
The battery attenuation model obtained by training in advance may be a battery attenuation model obtained by analyzing and modeling through a neural network learning method, which is not particularly limited herein.
It can be understood that the embodiment of the application collects the related data of the target vehicle in the charge and discharge state, matches the attenuation degree model obtained by training in advance according to the identity of the target vehicle, inputs the data into the battery attenuation model, and calculates the actual attenuation degree of the power battery in real time through the battery attenuation model, so that a user can timely and accurately master the attenuation condition of the battery.
It should be noted that, the charging data includes not only the charging voltage, the charging current, and the charging time, but also the battery charging temperature, the first ambient temperature, and the charging times; the discharge data not only comprises discharge voltage, discharge current and discharge time, but also comprises battery discharge temperature, second environment temperature and discharge times; and calculates the actual attenuation degree of the power battery by the charge and discharge data, which is not particularly limited herein.
In one embodiment of the present application, the charging data includes a charging voltage, a charging current, and a charging time, the discharging data includes a discharging voltage, a discharging current, and a discharging time, the charging data and the discharging data are input into a battery attenuation model, and an actual attenuation degree of a power battery in any vehicle is output, including: calculating actual charging power according to the charging voltage, the charging current and the charging time; calculating actual discharge power according to the discharge voltage, the discharge current and the discharge time; and calculating the ratio of the actual charging power to the actual discharging power, and calculating the actual attenuation degree of the power battery according to the ratio.
It can be understood that in the embodiment of the application, the actual charging power is calculated according to the charged related data, the actual discharging power is calculated according to the discharged related data, then the battery attenuation degree is calculated according to the ratio of the time charging power to the actual discharging power, and the battery attenuation degree is calculated according to the ratio of the discharging power to the charging power by fully considering the attenuation degree, so that the obtained data has more practical value and is more accurate.
Specifically, the cloud end analyzes and models according to the collected charging and discharging data of the battery of the electric automobile through a neural network learning method, the attenuation value is alpha, and the attenuation value is alpha
Figure BDA0003915436020000071
Wherein the charging formula calculates to obtain the charging electric power
Figure BDA0003915436020000072
In another embodiment of the present application, the charging data further includes a battery charging temperature, a first ambient temperature, and a number of times of charging, the discharging data further includes a battery discharging temperature, a second ambient temperature, and a number of times of discharging, and calculating an actual attenuation degree of the power battery according to the ratio includes: matching the attenuation degree correction coefficient according to the battery charging temperature, the first environment temperature, the charging times, the battery discharging temperature, the second environment temperature and/or the discharging times; and calculating the actual attenuation degree of the power battery according to the ratio and the attenuation degree correction coefficient.
The first ambient temperature may be an ambient temperature at which the vehicle is in a charged state, which is not specifically limited herein.
The second ambient temperature may be an ambient temperature of the vehicle in a discharge state when the vehicle is in a driving process or in a long-time unused state, and is not particularly limited herein.
It can be understood that according to the embodiment of the application, the attenuation degree correction coefficient is calculated by external factors such as the charging temperature, the charging frequency, the ambient temperature and the discharging temperature of the battery, and the actual attenuation degree of the power battery is calculated according to the ratio of the time charging power to the actual discharging power and the attenuation degree correction coefficient, so that the influence of the external factors on the actual attenuation degree of the power battery is fully considered, and relevant correction is performed in the calculation, thereby ensuring the accuracy of the calculation.
Specifically, the attenuation degree of the output battery is obtained by considering possible influence factors of the battery, including the ambient temperature, the battery temperature and the number of times of charging and discharging the vehicle
Figure BDA0003915436020000073
And obtaining a vehicle battery attenuation degree model.
In the embodiment of the application, the battery attenuation model is trained based on historical charging data and historical discharging data of the target vehicle, and comprises the following steps: acquiring historical charging data and historical discharging data of a target vehicle in a preset SOC interval, wherein the historical charging data comprises historical charging voltage, historical charging current and historical charging time, and the historical discharging data comprises historical discharging voltage, historical discharging current and historical discharging time; calculating one or more historical charging powers according to the historical charging voltage, the historical charging current and the historical charging time; calculating one or more historical discharge powers according to the historical discharge voltage, the historical discharge current and the historical discharge time; calculating the ratio of the historical charging power to the historical discharging power, calculating one or more attenuation degrees of the power battery by using the ratio, and training a neural network by using the one or more attenuation degrees to obtain a battery attenuation model.
It can be understood that, in the embodiment of the present application, historical charging data and historical discharging data of the target vehicle in the SOC interval are obtained, historical charging and discharging power is calculated according to the historical charging and discharging voltage, the historical charging and discharging current and the historical charging and discharging time in the historical data, a plurality of historical attenuation degrees are obtained by calculating the ratio of the historical charging power to the historical discharging power, a neural network is trained through the attenuation degrees, a battery attenuation model is obtained, the battery attenuation degree model is continuously optimized, and finally a battery attenuation degree with higher accuracy is obtained.
Specifically, through real-time data acquisition of an electric automobile, the acquired data are analyzed in real time at the cloud, the electric power of the vehicle discharge is calculated in real time at the cloud, the electric power of the last charge of the cloud is extracted and stored, the electric power of the vehicle discharge in the same electric quantity interval and the electric power of the charge are input into an established battery attenuation degree model, the real-time battery attenuation degree of the automobile is obtained, and the real-time battery attenuation degree is calculated and monitored in real time at the cloud through the calculated real-time battery attenuation degree.
In this embodiment of the present application, the historical charging data further includes a historical battery charging temperature, a historical first ambient temperature and a historical charging frequency, the historical discharging data further includes a historical battery discharging temperature, a historical second ambient temperature and a historical discharging frequency, and the calculating of the ratio is utilized to obtain one or more attenuation degrees of the power battery, including: calculating according to the historical battery charging temperature, the historical first environmental temperature, the historical charging times, the historical battery discharging temperature, the historical second environmental temperature and/or the historical discharging times to obtain a damping degree correction coefficient; and calculating one or more attenuation degrees of the power battery by using the attenuation degree correction coefficient and the ratio.
It can be understood that, in the embodiment of the present application, the historical attenuation correction coefficient is obtained according to the historical relevant battery charging temperature, the environmental temperature, the discharge temperature and the like, and the plurality of attenuation degrees of the power battery are obtained according to the historical attenuation correction coefficient and the ratio of the historical charging power to the historical discharge power, and the battery attenuation degree with higher accuracy is obtained by continuously optimizing the battery attenuation degree model in consideration of the influence of the external factors on the actual attenuation degree of the power battery.
In the embodiment of the application, after outputting the actual attenuation degree of the power battery in the target vehicle, the method further includes: and optimizing a battery attenuation model by using the actual attenuation degree.
It can be understood that the embodiment of the application optimizes the battery attenuation model by using the actual attenuation degree, so that the calculation result is more accurate, and a user can conveniently and accurately grasp the battery attenuation condition.
In step S103, if the actual attenuation degree is smaller than the preset abnormal threshold, the power battery is determined to be in the preset abnormal state, and the power battery of the target vehicle is generated for early warning, otherwise, the power battery of the target vehicle is determined to be in the preset normal state.
The preset anomaly threshold value may be an anomaly threshold value set in advance by a user, for example: when the battery degradation α is less than 80%, the battery will be considered to be in an abnormal state, and is not particularly limited herein.
It can be appreciated that when the actual attenuation degree is smaller than the abnormal threshold value, the embodiment of the application judges that the battery is abnormal and carries out early warning prompt, so that the real-time calculation and monitoring of the attenuation cloud of the power battery of the electric automobile are realized, a user can timely and accurately grasp the attenuation state of the battery, potential safety hazards caused by attenuation of the battery are avoided, the detection time and maintenance cost are reduced, and the use safety of the electric automobile of a customer is improved.
According to the power battery monitoring method of the electric automobile, related data of a target automobile in a charging and discharging state are collected, a attenuation degree model which is trained in advance is matched according to the identity of the target automobile, the data are input into a battery attenuation model, the actual attenuation degree of the power battery is calculated in real time through the battery attenuation model, and when the actual attenuation value is within a normal threshold value, the power battery of the automobile is judged to be in a normal state; when the actual attenuation degree is smaller than the abnormal threshold value, the battery is judged to be abnormal and early warning prompt is carried out, so that real-time calculation and monitoring of the attenuation cloud of the power battery of the electric automobile are realized, a user can timely and accurately grasp the attenuation state of the battery, potential safety hazards caused by attenuation of the battery are avoided, the detection time and maintenance cost are reduced, and the use safety of the electric automobile of the customer is improved. Therefore, the problems that a user cannot timely and accurately master the attenuation condition of the battery, the efficiency is extremely low, the time and the cost are wasted, the safety of the user in use is reduced, the use experience of the user is reduced and the like in the related technology are solved.
The following describes a method for monitoring the power battery of an electric vehicle in detail with reference to fig. 2, 3, 4 and 5, specifically as follows:
the first step: the invention aims to provide a method for calculating and monitoring the attenuation of a power battery of an electric automobile in real time, which needs to acquire data of a first type: charging data of the electric automobile under the working condition of a standing state: v (V) Charging voltage 、I Charging current 、P Charging temperature 、T Charging time 、SOC 1 initial charging 、SOC 2 end of charging The number of times of charging, the temperature of the rechargeable battery, and the temperature of the charging environment; acquisition data type two: discharge data of the electric automobile in a running or discharging state: v (V) Discharge voltage 、I Discharge current 、P Discharge temperature 、T Discharge time 、SOC 1 initial discharge 、SOC 2 end of discharge Battery discharge temperature, discharge ambient temperature, state of charge, state of discharge, vehicle VIN (Vehicle Identification Number, vehicle identification code) code.
And a second step of: according to the data to be acquired in the first step, the cloud end is configured to acquire the data in real time, and the data acquisition requirements are as follows: when the electric automobile is in a static charging state, the judgment condition is that when the vehicle speed is 0, and the vehicle charging state is equal to a parking charging state, charging data are collected in real time through the automobile machine 4G: v (V) Charging voltage 、I Charging current 、P Charging temperature 、T Charging time 、SOC 1 initial charging 、SOC 2 end of charging Charging times, rechargeable battery temperature, charging environment temperature, and charging vehicle VIN code; in addition to collecting data of the stationary state of the vehicle, data of the discharging state of the vehicle needs to be collected, and a judgment bar is neededWhen the direct current to external discharge state is equal to the discharge state, recording the discharge data of the electric automobile in the running or discharge state: v (V) Discharge voltage 、I Discharge current 、P Discharge temperature 、T Discharge time 、SOC 1 initial discharge 、SOC 2 end of discharge Battery discharge temperature, discharge ambient temperature, discharge vehicle VIN code;
and a third step of: according to the charging and discharging data acquired by the vehicle end, real-time data analysis and synchronization are carried out at the cloud end, and according to the charging data acquired by the 4G in the previous two steps: v (V) Charging voltage 、I Charging current 、P Charging temperature 、T Charging time 、SOC 1 initial charging 、SOC 2 end of charging The number of charging times, the battery temperature, the charging environment temperature and the vehicle type VIN code; besides collecting data of a vehicle standing state, collecting data of the vehicle in a discharging state, judging that the discharging state is equal to the discharging state when the direct current is opposite to the external discharging state, and recording the discharging data of the electric automobile in the driving or discharging state: v (V) Discharge voltage 、I Discharge current 、P Discharge temperature 、T Discharge time 、SOC 1 initial discharge 、SOC 2 end of discharge And after the discharge temperature of the battery, the discharge environment temperature and the vehicle VIN code are analyzed at the cloud, synchronizing offline data, synchronizing the charging and discharging data of the electric automobile to an offline server, performing neural network training and establishing a corresponding model.
As shown in fig. 3, the automobile battery attenuation modeling flow chart is as follows: firstly, relevant data are extracted from HIVE (data warehouse tool) by using an HDFS (Hadoop Distributed File System, distributed file system) for batch processing, then a nerve network is constructed by using MYSQL (relational database management system) and SPARK (big data analysis engine), and data are input into scenes such as online data analysis, stream calculation, machine learning, graph calculation and the like, and a battery attenuation degree model is output.
Fourth step: according to the collected mass electric vehicle charging and discharging data, a neural network is used for obtaining a battery attenuation model, and a charging electric power is obtained through calculation according to a charging formula
Figure BDA0003915436020000101
Meanwhile, the collected mass electric vehicle discharge data are used for obtaining the discharge electric power +.>
Figure BDA0003915436020000102
The influence factors comprise the charge and discharge environment temperature, the charge and discharge battery temperature and the charge and discharge times of the vehicle.
The neural network learns from the input layer to the hidden hiding layer and finally to the output layer to obtain the attenuation degree of the output battery
Figure BDA0003915436020000103
The neural network is trained through massive data samples, so that battery electric power with higher accuracy under the standing charging working condition and battery electric power with higher accuracy under the discharging state can be obtained, and an accurate better battery attenuation degree model is obtained through inputting the ambient temperature, the battery temperature and the vehicle charging times and training the neural network.
Fifth step: and training according to the mass data to obtain battery attenuation alpha data, and then continuously accumulating the mass data through the electric automobile to continuously optimize and train a battery attenuation alpha model to obtain the battery attenuation alpha with better accuracy.
Sixth step: as shown in fig. 4 and fig. 5, real-time calculation of the attenuation degree of the electric battery of the vehicle is realized through real-time data acquisition of the electric vehicle, static charging data of the electric vehicle is uploaded to a cloud platform in real time through a vehicle end data acquisition configuration module, and the cloud platform adds real-time data of the vehicle to a battery attenuation model obtained in the fifth step: v (V) Charging voltage 、I Charging current 、P Charging temperature 、T Charging time 、SOC 1 initial charging 、SOC 2 end of charging The number of charges, the battery temperature, the charging ambient temperature, the vehicle VIN code; and discharge data of the electric automobile in a running or discharging state: v (V) Discharge voltage 、I Discharge current 、P Discharge temperature 、T Discharge time 、SOC 1 initial discharge 、SOC 2 end of discharge Battery discharge temperature and vehicleAnd the vehicle VIN codes are input into the Flink together at the cloud end for real-time calculation, so that the real-time attenuation degree of the battery is obtained.
Seventh step: through electric automobile Flink real-time calculation, the attenuation degree of the obtained vehicle electric battery is monitored at the cloud according to the attenuation degree, when the attenuation degree alpha of the battery is smaller than 80%, the battery is considered to be in an abnormal state, and according to the abnormal state, monitoring and early warning are carried out at the cloud, and the attenuation degree alpha value of the battery is displayed in real time in a non-abnormal state and is used for displaying a data display board.
In summary, in the embodiment of the application, cloud real-time data are utilized to collect relevant data of electric vehicle power battery discharge, the power battery attenuation degree is calculated in real time through the neural network attenuation model, and the battery attenuation degree is calculated by utilizing the neural network model and the collected real-time data, so that the electric vehicle real-time battery attenuation degree calculation and monitoring are realized.
Next, a power battery monitoring device of an electric vehicle according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 6 is a block schematic diagram of a power battery monitoring device of an electric vehicle according to an embodiment of the present application.
As shown in fig. 6, the power battery monitoring device 10 of the electric vehicle includes: a receiving module 100, a processing module 200 and a decision module 300.
The receiving module 100 is configured to receive charging data and discharging data collected by a target vehicle in a preset state of charge SOC interval; the processing module 200 is used for matching the battery attenuation model obtained by training in advance according to the identity of the target vehicle, inputting the charging data and the discharging data into the battery attenuation model, and outputting the actual attenuation degree of the power battery in the target vehicle; the determining module 300 is configured to determine that the power battery is in a preset abnormal state if the actual attenuation degree is smaller than a preset abnormal threshold, generate a power battery of the target vehicle to perform early warning prompt, and otherwise determine that the power battery of the target vehicle is in a preset normal state.
In the embodiment of the present application, the processing module 200 is used for: calculating actual charging power according to the charging voltage, the charging current and the charging time; calculating actual discharge power according to the discharge voltage, the discharge current and the discharge time; and calculating the ratio of the actual charging power to the actual discharging power, and calculating the actual attenuation degree of the power battery according to the ratio.
In the embodiment of the present application, the processing module 200 is further configured to: matching the attenuation degree correction coefficient according to the battery charging temperature, the first environment temperature, the charging times, the battery discharging temperature, the second environment temperature and/or the discharging times; and calculating the actual attenuation degree of the power battery according to the ratio and the attenuation degree correction coefficient.
In the embodiment of the present application, the processing module 200 is further configured to: acquiring historical charging data and historical discharging data of a target vehicle in a preset SOC interval, wherein the historical charging data comprises historical charging voltage, historical charging current and historical charging time, and the historical discharging data comprises historical discharging voltage, historical discharging current and historical discharging time; calculating one or more historical charging powers according to the historical charging voltage, the historical charging current and the historical charging time; calculating one or more historical discharge powers according to the historical discharge voltage, the historical discharge current and the historical discharge time; calculating the ratio of the historical charging power to the historical discharging power, calculating one or more attenuation degrees of the power battery by using the ratio, and training a neural network by using the one or more attenuation degrees to obtain a battery attenuation model.
In the embodiment of the present application, the processing module 200 is further configured to: calculating according to the historical battery charging temperature, the historical first environmental temperature, the historical charging times, the historical battery discharging temperature, the historical second environmental temperature and/or the historical discharging times to obtain a damping degree correction coefficient; and calculating one or more attenuation degrees of the power battery by using the attenuation degree correction coefficient and the ratio.
In the embodiment of the present application, the processing module 200 is further configured to: and optimizing a battery attenuation model by using the actual attenuation degree.
It should be noted that the foregoing explanation of the embodiment of the power battery monitoring method of the electric vehicle is also applicable to the power battery monitoring device of the electric vehicle in this embodiment, and will not be repeated here.
According to the power battery monitoring device of the electric automobile, related data of a target automobile in a charging and discharging state are collected, a attenuation degree model which is trained in advance is matched according to the identity of the target automobile, the data are input into a battery attenuation model, the actual attenuation degree of the power battery is calculated in real time through the battery attenuation model, and when the actual attenuation value is within a normal threshold value, the power battery of the automobile is judged to be in a normal state; when the actual attenuation degree is smaller than the abnormal threshold value, the battery is judged to be abnormal and early warning prompt is carried out, so that real-time calculation and monitoring of the attenuation cloud of the power battery of the electric automobile are realized, a user can timely and accurately grasp the attenuation state of the battery, potential safety hazards caused by attenuation of the battery are avoided, the detection time and maintenance cost are reduced, and the use safety of the electric automobile of the customer is improved. Therefore, the problems that a user cannot timely and accurately master the attenuation condition of the battery, the efficiency is extremely low, the time and the cost are wasted, the safety of the user in use is reduced, the use experience of the user is reduced and the like in the related technology are solved.
Fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:
memory 701, processor 702, and computer programs stored on memory 701 and executable on processor 702.
The processor 702 implements the power battery monitoring method of the electric vehicle provided in the above embodiment when executing the program.
Further, the server further includes:
a communication interface 703 for communication between the memory 701 and the processor 702.
Memory 701 for storing a computer program executable on processor 702.
The memory 701 may include high-speed RAM (Random Access Memory ) memory, and may also include non-volatile memory, such as at least one disk memory.
If the memory 701, the processor 702, and the communication interface 703 are implemented independently, the communication interface 703, the memory 701, and the processor 702 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component, external device interconnect) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 7, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 701, the processor 702, and the communication interface 703 are integrated on a chip, the memory 701, the processor 702, and the communication interface 703 may communicate with each other through internal interfaces.
The processor 702 may be a CPU (Central Processing Unit ) or ASIC (Application Specific Integrated Circuit, application specific integrated circuit) or one or more integrated circuits configured to implement embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the power battery monitoring method of the electric vehicle.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable gate arrays, field programmable gate arrays, and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (14)

1. A method for monitoring a power battery of an electric vehicle, wherein the method is applied to a server, and the method comprises the following steps:
receiving charging data and discharging data acquired by a target vehicle in a preset state of charge (SOC) interval;
according to the battery attenuation model obtained by the pre-training of the identity identification matching of the target vehicle, the charging data and the discharging data are input into the battery attenuation model, and the actual attenuation degree of the power battery in the target vehicle is output;
If the actual attenuation degree is smaller than a preset abnormal threshold value, judging that the power battery is in a preset abnormal state, generating a power battery of the target vehicle to perform early warning prompt, otherwise, judging that the power battery of the target vehicle is in a preset normal state.
2. The method of claim 1, wherein the charge data comprises a charge voltage, a charge current, and a charge time, the discharge data comprises a discharge voltage, a discharge current, and a discharge time, the inputting the charge data and the discharge data into the battery attenuation model outputs an actual attenuation of a power battery in any vehicle, comprising:
calculating actual charging power according to the charging voltage, the charging current and the charging time;
calculating actual discharge power according to the discharge voltage, the discharge current and the discharge time;
and calculating the ratio of the actual charging power to the actual discharging power, and calculating the actual attenuation degree of the power battery according to the ratio.
3. The method of claim 2, wherein the charge data further comprises a battery charge temperature, a first ambient temperature, and a number of charges, the discharge data further comprises a battery discharge temperature, a second ambient temperature, and a number of discharges, and wherein calculating the actual attenuation of the power battery based on the ratio comprises:
Matching a damping degree correction coefficient according to the battery charging temperature, the first ambient temperature, the charging times, the battery discharging temperature, the second ambient temperature and/or the discharging times;
and calculating the actual attenuation degree of the power battery according to the ratio and the attenuation degree correction coefficient.
4. The method of claim 1, wherein the battery decay model is trained based on historical charge data and historical discharge data of the target vehicle, comprising:
acquiring historical charging data and historical discharging data of the target vehicle in a preset SOC interval, wherein the historical charging data comprises historical charging voltage, historical charging current and historical charging time, and the historical discharging data comprises historical discharging voltage, historical discharging current and historical discharging time;
calculating one or more historical charging powers according to the historical charging voltage, the historical charging current and the historical charging time;
calculating one or more historical discharge powers according to the historical discharge voltage, the historical discharge current and the historical discharge time;
calculating the ratio of the historical charging power to the historical discharging power, calculating one or more attenuation degrees of the power battery by using the ratio, and training a neural network by using the one or more attenuation degrees to obtain the battery attenuation model.
5. The method of claim 4, wherein the historical charge data further comprises a historical battery charge temperature, a historical first ambient temperature, and a historical charge number, the historical discharge data further comprises a historical battery discharge temperature, a historical second ambient temperature, and a historical discharge number, the calculating using the ratio one or more attenuations of the power battery comprises:
calculating according to the historical battery charging temperature, the historical first environment temperature, the historical charging times, the historical battery discharging temperature, the historical second environment temperature and/or the historical discharging times to obtain a damping degree correction coefficient;
and calculating one or more attenuation degrees of the power battery by using the attenuation degree correction coefficient and the ratio.
6. The method according to any one of claims 1 to 4, characterized by further comprising, after outputting the actual degree of attenuation of the power battery in the target vehicle:
and optimizing the battery attenuation model by using the actual attenuation degree.
7. A power battery monitoring device for an electric vehicle, wherein the device is applied to a server, and the device comprises the following steps:
The receiving module is used for receiving charging data and discharging data acquired by the target vehicle in a preset state of charge (SOC) interval;
the processing module is used for matching a battery attenuation model obtained through training in advance according to the identity of the target vehicle, inputting the charging data and the discharging data into the battery attenuation model, and outputting the actual attenuation degree of the power battery in the target vehicle;
and the judging module is used for judging that the power battery is in a preset abnormal state if the actual attenuation degree is smaller than a preset abnormal threshold value, generating the power battery of the target vehicle for early warning prompt, and otherwise judging that the power battery of the target vehicle is in a preset normal state.
8. The apparatus of claim 7, wherein the processing module is to:
calculating actual charging power according to the charging voltage, the charging current and the charging time;
calculating actual discharge power according to the discharge voltage, the discharge current and the discharge time;
and calculating the ratio of the actual charging power to the actual discharging power, and calculating the actual attenuation degree of the power battery according to the ratio.
9. The apparatus of claim 8, wherein the processing module is further to:
Matching a damping degree correction coefficient according to the battery charging temperature, the first environment temperature, the charging times, the battery discharging temperature, the second environment temperature and/or the discharging times;
and calculating the actual attenuation degree of the power battery according to the ratio and the attenuation degree correction coefficient.
10. The apparatus of claim 7, wherein the processing module is further to:
acquiring historical charging data and historical discharging data of the target vehicle in a preset SOC interval, wherein the historical charging data comprises historical charging voltage, historical charging current and historical charging time, and the historical discharging data comprises historical discharging voltage, historical discharging current and historical discharging time;
calculating one or more historical charging powers according to the historical charging voltage, the historical charging current and the historical charging time;
calculating one or more historical discharge powers according to the historical discharge voltage, the historical discharge current and the historical discharge time;
calculating the ratio of the historical charging power to the historical discharging power, calculating one or more attenuation degrees of the power battery by using the ratio, and training a neural network by using the one or more attenuation degrees to obtain the battery attenuation model.
11. The apparatus of claim 10, wherein the processing module is further to:
calculating according to the historical battery charging temperature, the historical first environment temperature, the historical charging times, the historical battery discharging temperature, the historical second environment temperature and/or the historical discharging times to obtain a damping degree correction coefficient;
and calculating one or more attenuation degrees of the power battery by using the attenuation degree correction coefficient and the ratio.
12. The apparatus of claim 7, wherein the processing module is further to:
and optimizing the battery attenuation model by using the actual attenuation degree.
13. A server, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the power battery monitoring method of an electric vehicle according to any one of claims 1-6.
14. A computer-readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for realizing the power battery monitoring method of an electric vehicle according to any one of claims 1 to 6.
CN202211338422.0A 2022-10-28 2022-10-28 Power battery monitoring method and device for electric automobile, server and medium Pending CN116080470A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211338422.0A CN116080470A (en) 2022-10-28 2022-10-28 Power battery monitoring method and device for electric automobile, server and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211338422.0A CN116080470A (en) 2022-10-28 2022-10-28 Power battery monitoring method and device for electric automobile, server and medium

Publications (1)

Publication Number Publication Date
CN116080470A true CN116080470A (en) 2023-05-09

Family

ID=86185661

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211338422.0A Pending CN116080470A (en) 2022-10-28 2022-10-28 Power battery monitoring method and device for electric automobile, server and medium

Country Status (1)

Country Link
CN (1) CN116080470A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117388732A (en) * 2023-07-07 2024-01-12 江苏华翊成电气科技有限公司 High-power density direct-current power supply safety monitoring method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117388732A (en) * 2023-07-07 2024-01-12 江苏华翊成电气科技有限公司 High-power density direct-current power supply safety monitoring method and system

Similar Documents

Publication Publication Date Title
EP3748380A1 (en) Battery capacity prediction system using charge and discharge cycles of a battery to predict capacity variations, and associated method
WO2020224724A1 (en) Server-side characterisation of rechargeable batteries
CN109001640B (en) Data processing method and device for power battery
CN108556682B (en) Driving range prediction method, device and equipment
CN109100655B (en) Data processing method and device for power battery
CN113696786B (en) Battery equalization method and system
CN103576096A (en) Real-time assessment method and device for residual capacity of power battery of electric automobile
CN113189495B (en) Battery health state prediction method and device and electronic equipment
CN110568360B (en) Lithium battery aging diagnosis method based on fuzzy logic algorithm
CN112904219B (en) Big data-based power battery health state prediction method
CN112101597A (en) Electric vehicle leasing operation platform vehicle fault pre-judging system, method and device
CN113219336B (en) Battery degradation determination system, method, and non-transitory storage medium storing program
CN106998086A (en) MW class energy-accumulating power station battery management method and its system
CN108710087A (en) A kind of pure electric automobile battery state-of-health measures device and its measures method
CN116047300A (en) Controller for predicting characteristic parameters of battery and method thereof
CN116080470A (en) Power battery monitoring method and device for electric automobile, server and medium
US20220283229A1 (en) Learning method, state estimation method, and state estimation device for state estimation model of secondary battery
Pau et al. Microcontroller architectures for battery state of charge prediction with tiny neural networks
CN115327380A (en) Battery failure diagnosis method, storage medium, and electronic device
CN208350965U (en) A kind of pure electric automobile battery state-of-health measures device
US20220283235A1 (en) Learning method and open circuit voltage estimation method of open circuit voltage estimation model of secondary battery, and state estimation method of secondary battery
CN116298944A (en) Charging remaining time display control method, device, equipment, vehicle and medium
CN113619447B (en) Method for predicting state of charge of battery of electric automobile
US12019122B2 (en) Learning method, state estimation method, and state estimation device for state estimation model of secondary battery
CN112035942B (en) Driving behavior-based energy consumption simulation method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination