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CN-121995233-A - Cloud platform and method for determining safe charging current

CN121995233ACN 121995233 ACN121995233 ACN 121995233ACN-121995233-A

Abstract

The application discloses a cloud platform and a method for determining safe charging current, which relate to the technical field of battery safety management, wherein the cloud platform comprises a data acquisition device and a cloud processor, and the data acquisition device is used for acquiring current working condition data and historical working condition data of a target battery from a vehicle-mounted battery management system; the cloud processor is used for determining a first working condition data increment according to current working condition data and historical working condition data, inputting the first working condition data increment into the direct current internal resistance increasing model to obtain an increasing rate output by the direct current internal resistance increasing model, determining the current negative direct current internal resistance of the target battery according to the increasing rate and the historical negative direct current internal resistance of the target battery, determining the safe charging current of the target battery according to the current negative direct current internal resistance, solving the technical problem of accurately evaluating the charging capacity of the battery in a safe state, and improving the evaluation accuracy of the safe charging capacity of the battery.

Inventors

  • Ren Mingxi
  • Jiang Mingjuehui
  • Xiong Tiedan
  • GAO PO
  • MA RUIJUN

Assignees

  • 中创新航科技集团股份有限公司

Dates

Publication Date
20260508
Application Date
20260302

Claims (18)

  1. 1. The cloud platform is characterized by comprising a data acquisition unit and a cloud processor, wherein the data acquisition unit is respectively in communication connection with the cloud processor and a vehicle-mounted battery management system, The data acquisition device is used for acquiring current working condition data and historical working condition data of a target battery from the vehicle-mounted battery management system; The cloud processor is used for: determining a first working condition data increment according to the current working condition data and the historical working condition data; Inputting the first working condition data increment into a direct current internal resistance increasing model to obtain an increasing rate output by the direct current internal resistance increasing model, wherein the direct current internal resistance increasing model is used for determining the increasing rate of the negative direct current internal resistance of the battery under different working conditions; determining the current negative direct current internal resistance of the target battery according to the growth rate and the historical negative direct current internal resistance of the target battery; and determining the safe charging current of the target battery according to the current negative DC internal resistance.
  2. 2. The cloud platform of claim 1, wherein the cloud processor is further configured to: and determining the charging strategy of the target battery according to the safe charging current.
  3. 3. The cloud platform of claim 2, further comprising a data transmitter for: And sending the charging strategy to a user terminal to indicate the user terminal to display the charging strategy on a display page corresponding to the user terminal after receiving the charging strategy, wherein the user terminal comprises a personal terminal or a third party terminal.
  4. 4. The cloud platform of claim 3, wherein the cloud platform is respectively in communication connection with a plurality of vehicle-mounted battery management systems, the plurality of vehicle-mounted battery management systems correspond to different user terminals, and the cloud platform sends the obtained charging strategy of the battery corresponding to any vehicle-mounted battery management system to the user terminal corresponding to any vehicle-mounted battery management system after processing the data corresponding to any vehicle-mounted battery management system.
  5. 5. The cloud platform of claim 1, wherein the cloud processor is further configured to: Obtaining test data of a test battery, wherein the model of the test battery is the same as that of the target battery; And constructing the direct current internal resistance growth model according to the test data.
  6. 6. The cloud platform of claim 5, wherein the cloud processor is further configured to obtain test data for testing the battery by: Performing accelerated life test on the test battery according to a test variable to obtain an association relation between the negative DC internal resistance of the test battery and the test variable, wherein the accelerated life test is used for testing the increase rate of the negative DC internal resistance of the test battery under a preset working condition; And determining the test data according to the test variable, the negative DC internal resistance of the test battery corresponding to the test variable and the association relation.
  7. 7. The cloud platform of claim 6, wherein the cloud processor is further configured to obtain the association between the internal dc resistance of the negative electrode of the test battery and the test variable by: Under the condition that the test variable is determined to be the battery storage temperature, acquiring a first test temperature, a second test temperature and a third test temperature which are determined from a test temperature range, wherein the first test temperature is smaller than the second test temperature, and the second test temperature is smaller than the third test temperature; recording a first curve, a second curve and a third curve of the negative direct current internal resistance of the test battery, which are stored with time, of the test battery at the first test temperature, the second test temperature and the third test temperature respectively; And determining the association relation between the negative direct current internal resistance of the test battery and the battery storage temperature according to an Arrhenius equation, the first curve, the second curve and the third curve.
  8. 8. The cloud platform of claim 7, wherein the cloud processor is further configured to determine an association between the negative dc internal resistance of the test battery and the battery storage temperature by: Determining a first growth rate according to the first curve, determining a second growth rate according to the second curve, and determining a third growth rate according to the third curve, wherein the first growth rate represents the growth rate of the negative direct current internal resistance of the test battery when the first test temperature is stored, the second growth rate represents the growth rate of the negative direct current internal resistance of the test battery when the second test temperature is stored, and the third growth rate represents the growth rate of the negative direct current internal resistance of the test battery when the third test temperature is stored; Substituting the first growth rate, the second growth rate and the third growth rate into the Arrhenius equation to obtain a first fitting point, a second fitting point and a third fitting point; fitting a mathematical formula to the first fitting point, the second fitting point and the third fitting point to obtain a first mathematical expression; And determining the association relation between the negative direct current internal resistance of the test battery and the battery storage temperature according to the first mathematical expression.
  9. 9. The cloud platform of claim 6, wherein the cloud processor is further configured to obtain the association between the internal dc resistance of the negative electrode of the test battery and the test variable by: under the condition that the test variable is determined to be the battery circulation temperature, acquiring a fourth test temperature, a fifth test temperature and a sixth test temperature which are determined from a test temperature range, wherein the fourth test temperature is smaller than the fifth test temperature, and the fifth test temperature is smaller than the sixth test temperature; Recording a fourth curve, a fifth curve and a sixth curve of the negative direct current internal resistance of the test battery which are increased along with time under the condition that the test battery is subjected to charge-discharge circulation at the fourth test temperature, the fifth test temperature and the sixth test temperature respectively; And determining the association relation between the negative direct current internal resistance of the test battery and the battery circulation temperature according to the Arrhenius equation and the fourth curve, the fifth curve and the sixth curve.
  10. 10. The cloud platform of claim 6, wherein said cloud processor is further configured to construct said direct current internal resistance growth model by: obtaining a plurality of mathematical functions based on the mathematical functions corresponding to different association relations; determining a target mathematical function according to the plurality of mathematical functions, wherein the input of the target mathematical function is a plurality of variable values in battery working condition data, and the output of the target mathematical function is the increase rate of negative DC internal resistance of the battery; and constructing the direct current internal resistance growth model according to the target mathematical function.
  11. 11. The cloud platform of claim 10, wherein the cloud processor is further configured to determine the objective mathematical function by: obtaining a basic aging rate constant and an aging state function of the test battery, wherein the basic aging rate constant is used for representing inherent aging characteristics of the test battery, and the aging state function is used for representing the aging rate changing characteristics of the test battery along with aging degree; Converting the function expression of each mathematical function into a factor term, and multiplying a plurality of factor terms to obtain a comprehensive factor term; Multiplying the comprehensive factor item, the basic aging rate constant and the aging state function to obtain a function expression of the objective mathematical function.
  12. 12. The cloud platform of claim 1, wherein the cloud processor is further configured to determine the first operating condition data delta by: acquiring first historical working condition data at a first moment from the historical working condition data, wherein the first moment is the latest working condition data recording moment; and determining the first working condition data increment according to the variation of the current working condition data and the first historical working condition data.
  13. 13. The cloud platform of claim 12, wherein said cloud processor is further configured to determine a current negative dc internal resistance of said target battery by: Acquiring a first historical negative direct current internal resistance corresponding to the first moment from the historical negative direct current internal resistance; Determining a first negative direct current internal resistance increment of the target battery according to the product of the first historical negative direct current internal resistance and the growth rate; Determining the current negative direct current internal resistance according to the sum of the first negative direct current internal resistance increment and the first historical negative direct current internal resistance; or, obtaining initial historical negative DC internal resistance corresponding to the initial moment from the historical negative DC internal resistance; determining a second negative direct current internal resistance increment of the target battery according to the product of the initial historical negative direct current internal resistance and the growth rate; And determining the current negative direct current internal resistance according to the sum of the second negative direct current internal resistance increment and the initial historical negative direct current internal resistance.
  14. 14. The cloud platform of claim 13, wherein the cloud processor is further configured to: acquiring the voltage between the negative electrode of the target battery and a copper wire, wherein the copper wire is arranged between the negative electrode of the target battery and a negative electrode diaphragm; obtaining a test current for testing the negative DC internal resistance of the target battery; And determining the initial negative direct current internal resistance according to the ratio of the voltage to the test current.
  15. 15. The cloud platform of claim 1, wherein the cloud processor is further configured to determine the safe charging current of the target battery by: Determining corresponding critical currents of the target battery under the condition of charging according to different negative direct current internal resistances, wherein negative lithium precipitation occurs to the target battery under the condition that the charging current is larger than the critical currents; Obtaining charging currents of the target battery when negative electrode lithium precipitation occurs under the condition of charging according to different negative electrode direct current internal resistances, and obtaining different critical currents; Establishing a target mapping relation function according to the corresponding relation between the different critical currents and the different negative DC internal resistances; And determining the current critical current corresponding to the current negative DC internal resistance according to the target mapping relation function, and determining the current critical current as the safe charging current.
  16. 16. The cloud platform of claim 1, wherein the cloud processor is further configured to: Determining a second working condition data increment based on preset working condition data and the current working condition data, wherein the preset working condition data are represented as working condition data simulated at a preset moment, and the preset moment is after the current moment; Inputting the second working condition data increment into the direct-current internal resistance growth model to obtain a predicted growth rate output by the direct-current internal resistance growth model; Determining the predicted negative direct current internal resistance of the target battery at the preset moment according to the predicted growth rate and the current negative direct current internal resistance of the target battery; And determining the predicted safe charging current of the target battery at the preset time according to the predicted negative direct current internal resistance.
  17. 17. The cloud platform of claim 1, wherein the cloud processor is further configured to: Establishing backup data for the target battery, wherein the backup data comprises the direct current internal resistance growth model, the initial negative direct current internal resistance of the target battery and working condition data of the target battery, and the backup data is stored in a cloud server; And acquiring the latest working condition data of the target battery according to a preset period, and storing the latest working condition data into the backup data.
  18. 18. The method for determining the safe charging current is characterized by being applied to a cloud platform and comprising the following steps of: Collecting current working condition data and historical working condition data of a target battery from a vehicle-mounted battery management system; determining a first working condition data increment according to the current working condition data and the historical working condition data; Inputting the first working condition data increment into a direct current internal resistance increasing model to obtain an increasing rate output by the direct current internal resistance increasing model, wherein the direct current internal resistance increasing model is used for determining the increasing rate of the negative direct current internal resistance of the battery under different working conditions; determining the current negative direct current internal resistance of the target battery according to the growth rate and the historical negative direct current internal resistance of the target battery; and determining the safe charging current of the target battery according to the current negative DC internal resistance.

Description

Cloud platform and method for determining safe charging current Technical Field The application relates to the technical field of battery safety management, in particular to a cloud platform and a method for determining safe charging current. Background With the popularization of electric automobiles and energy storage power stations, a large number of lithium ion batteries are put into market use. The performance, particularly the charging capability, of a battery is continuously attenuated with the use time, the number of cycles, and the use condition. The evaluation of the battery health condition at the market end of car owners, car manufacturers, operation and maintenance and the like at present mainly depends on two traditional methods, namely, the simple calculation based on full charge capacity attenuation only focuses on the capacity retention rate, and the charge acceptance of the battery can not be accurately reflected. Secondly, the sensitivity to the negative electrode state is insufficient depending on the global direct current internal resistance estimated value provided by the battery management system, and the battery is often changed after obvious aging, so that early warning cannot be realized. In addition, the existing method is separated from the actual use condition of the user, and personalized evaluation prediction cannot be made for the specific battery. Accordingly, in the related art, there is a technical problem of how to accurately evaluate the charging capability of the battery in a safe state. Aiming at the technical problem of how to accurately evaluate the charging capability of a battery in a safe state in the related art, no effective solution has been proposed yet. Disclosure of Invention The embodiment of the application provides a cloud platform and a method for determining safe charging current, which are used for at least solving the technical problem of how to accurately evaluate the charging capability of a battery in a safe state in the related technology. According to one embodiment of the application, a cloud platform is provided, which comprises a data acquisition device and a cloud processor, wherein the data acquisition device is respectively in communication connection with the cloud processor and a vehicle-mounted battery management system, the data acquisition device is used for acquiring current working condition data and historical working condition data of a target battery from the vehicle-mounted battery management system, the cloud processor is used for determining a first working condition data increment according to the current working condition data and the historical working condition data, inputting the first working condition data increment into a direct current internal resistance growth model to obtain an increase rate output by the direct current internal resistance growth model, the direct current internal resistance growth model is used for determining an increase rate of negative direct current internal resistance of the battery under different working conditions, determining the current negative direct current internal resistance of the target battery according to the increase rate and the historical negative direct current internal resistance of the target battery, and determining safe charging current of the target battery according to the current negative direct current internal resistance. According to another embodiment of the application, a method for determining safe charging current is provided, and the method is applied to the cloud platform and comprises the steps of collecting current working condition data and historical working condition data of a target battery from a vehicle-mounted battery management system, determining a first working condition data increment according to the current working condition data and the historical working condition data, inputting the first working condition data increment into a direct current internal resistance increasing model to obtain an increasing rate output by the direct current internal resistance increasing model, wherein the direct current internal resistance increasing model is used for determining increasing rates of negative direct current internal resistances of the battery under different working conditions, determining current negative direct current internal resistance of the target battery according to the increasing rates and the historical negative direct current internal resistances of the target battery, and determining safe charging current of the target battery according to the current negative direct current internal resistance. The cloud platform comprises a data acquisition device and a cloud processor, wherein the data acquisition device is respectively in communication connection with the cloud processor and a vehicle-mounted battery management system, the data acquisition device is used for acquiring current working condition data and historical working condition