CN118017655A - Lithium battery charging and discharging process monitoring method, system, equipment and medium - Google Patents
Lithium battery charging and discharging process monitoring method, system, equipment and medium Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 227
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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- H01M10/00—Secondary cells; Manufacture thereof
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- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
- H01M10/443—Methods for charging or discharging in response to temperature
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
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- H—ELECTRICITY
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
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- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
- H02J7/007192—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature
- H02J7/007194—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature of the battery
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Abstract
The invention relates to the technical field of battery monitoring, and discloses a method, a system, equipment and a medium for monitoring a charging and discharging process of a lithium battery, which comprise the following steps: acquiring a set charging time length of a lithium battery; acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery from the T+N time; judging whether the lithium battery at the time of T+N is in an over-temperature/low-temperature state or not according to the estimated temperature value; inputting the temperature control characteristic data of the lithium battery into a second pre-configured machine learning model to obtain optimal charging data, and correcting the initial charging data of the lithium battery according to the optimal charging data; repeating the steps until Q=T+N, ending the cycle to finish the multi-time charging regulation and control of the lithium battery within the set charging duration; the invention can ensure the safety of the charging process of the lithium battery and ensure the safe charging of the lithium battery in a set time.
Description
Technical Field
The invention relates to the technical field of battery monitoring, in particular to a method, a system, equipment and a medium for monitoring a charging and discharging process of a lithium battery.
Background
With the rapid development of the fields of mobile electronic equipment, electric automobiles, renewable energy sources and the like, a lithium battery is attracting more attention as an efficient, lightweight and high-energy-density electricity storage technology; however, during the charging process of the lithium battery, the battery may be over-heated due to interference of external environment and other factors, and the battery may be exploded when serious, so that it becomes important to accurately monitor the charging and discharging process of the lithium battery; the traditional lithium battery monitoring method has some limitations, such as low monitoring precision, poor real-time performance, higher equipment cost and the like; therefore, there is a need to provide a more advanced and effective method, system, device and medium for monitoring the charge and discharge process of a lithium battery, so as to meet the stricter requirements on the performance, safety and the like of the lithium battery.
At present, most of the existing monitoring methods for the charging and discharging processes of the lithium battery are in-process monitoring, namely, when the charging of the lithium battery is abnormal, automatic control and regulation are performed, and the performance and safety of the lithium battery are difficult to effectively guarantee; there are, of course, some improved technical documents, for example, chinese patent publication No. CN115344074B discloses a lithium battery thermostatic control system based on big data, and such a method realizes thermostatic control of a lithium battery by adjusting the speed of a cooling fan; however, research and practical application of the above method and the prior art have found that the above method and the prior art have at least the following partial drawbacks:
(1) The use scene is limited, the future temperature change of the lithium battery cannot be predicted, and further, the future state of the lithium battery cannot be determined according to the predicted temperature;
(2) The optimal charging rate/optimal charging current of the lithium battery cannot be obtained on the premise of obtaining the predicted temperature, so that the self-adaptive adjustment of the charging process of the lithium battery is difficult, and the charging without stopping can not be performed while the safety of the charging process of the lithium battery is ensured; further, it is difficult to ensure that the lithium battery completes a safe charging process at a given time.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a method, a system, equipment and a medium for monitoring the charging and discharging process of a lithium battery.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A method for monitoring a charging and discharging process of a lithium battery, the method comprising:
s101: acquiring a set charging duration Q of a lithium battery, wherein Q is an integer greater than zero;
S102: acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery at the T+N time, wherein the monitoring data comprises initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of the lithium battery, and N is an integer larger than zero;
S103: judging whether the lithium battery at the time of T+N is in an over-temperature/low-temperature state or not according to the estimated temperature value, if not, continuously charging the lithium battery according to the initial charging data, enabling T=T+N+M, and returning to the step S101; if the temperature control characteristic data of the lithium battery appear, acquiring the temperature control characteristic data of the lithium battery, wherein M is an integer larger than zero;
S104: inputting temperature control characteristic data of the lithium battery into a second preconfigured machine learning model to obtain optimal charging data, correcting initial charging data of the lithium battery according to the optimal charging data, and charging the lithium battery according to the corrected initial charging data within a time range from T to T+N;
S105: repeating the steps S102-S104 until Q=T+N, ending the cycle to complete the multi-time charging regulation and control of the lithium battery within the set charging duration.
Further, the initial charging data is an initial charging current and an initial charging rate;
the acquisition logic of the temperature change coefficient is as follows:
extracting an ambient temperature value and a battery temperature value within a time span from T-N to T;
Carrying out formula calculation on an environment temperature value and a battery temperature value in a time span from T-N to T to obtain a temperature change coefficient; the calculation formula is as follows: ; wherein: /(I) Is the temperature change coefficient,/>Is the ambient temperature value at the ith time,/>Is the ambient temperature value at the i-1 th time,/>The battery temperature value at the i-th time,And Q is the time span from T-N to T for the battery temperature value at the i-1 th moment.
Further, the generating logic according to the preconfigured first machine learning model is as follows:
Acquiring historical temperature change data, and dividing the historical temperature change data into a predicted temperature training set and a predicted temperature testing set; the historical temperature change data comprises estimated temperature characteristic data for predicting an estimated temperature value and the corresponding estimated temperature value; the estimated temperature characteristic data comprise a plurality of groups of initial charging data of time spans, measured voltage values, environment temperature values, battery temperature values and temperature change coefficients;
Initializing a first regression network, taking predicted temperature characteristic data in a predicted temperature training set as input data of the first regression network, taking predicted temperature values in the predicted temperature training set as output data of the first regression network, and training the first regression network to obtain an initial first machine learning network;
And performing model verification on the initial first machine learning network by using the estimated temperature test set, and outputting the initial first machine learning network with the test error less than or equal to the preset test error as a preset first machine learning model.
Further, the determining whether the lithium battery at the time of t+n has an over-temperature/low-temperature state includes:
Acquiring a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 of the lithium battery, wherein Td1 is more than Td2;
Comparing the estimated temperature value with a preset upper temperature threshold Td1, and if the estimated temperature value is larger than Td1, judging that the lithium battery at the time of T+N is in an over-temperature state; if the estimated temperature value is smaller than or equal to Td1, comparing the estimated temperature value with a preset temperature lower limit threshold Td 2; if the estimated temperature value is greater than or equal to Td2, judging that the lithium battery at the moment T+N does not have an over-temperature/low-temperature state; if the estimated temperature value is smaller than Td2, the lithium battery at the time of T+N is judged to be in a low-temperature state.
Further, the temperature control characteristic data of the lithium battery comprises a first temperature difference and a second temperature difference, wherein the first temperature difference is a difference value between a predicted temperature value and a preset upper temperature threshold Td1, and the second temperature difference is a difference value between a predicted temperature value and a preset lower temperature threshold Td 2;
the second machine learning model is generated according to training of lithium battery test data, and the lithium battery test data at least comprises one of the relation between the optimal charging current and the temperature control characteristic data or the relation between the optimal charging rate and the temperature control characteristic data.
Further, the generation logic of the preconfigured second machine learning model is as follows:
Acquiring historical charging test data of the lithium battery, and dividing the historical charging test data into an optimal charging training set and an optimal charging test set; the historical charging test data comprises temperature control characteristic data and corresponding optimal charging current or optimal charging rate;
Initializing a second regression network, taking temperature control characteristic data in an optimal charging training set as input data of the second regression network, taking optimal charging current or optimal charging rate in the optimal charging training set as output data of the second regression network, and training the second regression network to obtain an initial second machine learning network;
And performing model verification on the initial second machine learning network by using the optimal charging test set, and outputting the initial second machine learning network with the test error less than or equal to the preset test error as a preconfigured second machine learning model.
Further, the generation logic of the relation between the optimal charging current and the temperature control characteristic data is as follows:
Step a1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step a2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging current R in initial charging data, enabling R=R+1 or R=R-1, and returning to the step a1, wherein R is an integer larger than zero;
Step a3: repeating the steps a1 to a2 until the first temperature difference or the second temperature difference is equal to the set first threshold value, ending the cycle, and taking the charging current corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
Step a4: binding the relation between the first temperature difference or the second temperature difference and the optimal charging current to obtain the relation between the optimal charging current and the temperature control characteristic data;
The generation logic of the relation between the optimal charging rate and the temperature control characteristic data is as follows:
Step b1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step b2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging rate V in initial charging data, enabling V=V+1 or V=V-1, and returning to the step b1, wherein V is an integer larger than zero;
Step b3: repeating the steps b1 to b2 until the first temperature difference or the second temperature difference is equal to the set second threshold value, ending the cycle, and taking the charging rate corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
step b4: and carrying out relation binding on the first temperature difference or the second temperature difference and the optimal charging rate to obtain the relation between the optimal charging rate and the temperature control characteristic data.
A lithium battery charge-discharge process monitoring system, comprising:
the data acquisition module is used for acquiring the set charging time length Q of the lithium battery, wherein Q is an integer greater than zero;
The temperature prediction module is used for acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery at the T+N time, wherein the monitoring data comprises initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of the lithium battery, and N is an integer larger than zero;
The state judging module is used for judging whether the lithium battery at the time of T+N is in an over-temperature/low-temperature state or not according to the estimated temperature value, if not, continuously charging the lithium battery according to the initial charging data, enabling T=T+N+M, and triggering the data acquisition module; if the temperature control characteristic data of the lithium battery appear, acquiring the temperature control characteristic data of the lithium battery, wherein M is an integer larger than zero;
The parameter correction module is used for inputting the temperature control characteristic data of the lithium battery into a second preconfigured machine learning model to obtain optimal charging data, correcting the initial charging data of the lithium battery according to the optimal charging data, and charging the lithium battery according to the corrected initial charging data within the time range from T to T+N;
and the self-adaptive control module is used for repeating the temperature prediction module to the parameter correction module until Q=T+N, and ending the cycle so as to finish the multi-time charging regulation and control of the lithium battery within the set charging duration.
An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, the processor implementing a lithium battery charging and discharging process monitoring method according to any one of the preceding claims when executing the computer program.
A computer readable storage medium having a computer program stored thereon, which when executed implements a lithium battery charging and discharging process monitoring method according to any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
The application discloses a method, a system, equipment and a medium for monitoring a charging and discharging process of a lithium battery, which comprise the following steps: acquiring a set charging time length of a lithium battery; acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery from the T+N time; judging whether the lithium battery at the time of T+N is in an over-temperature/low-temperature state or not according to the estimated temperature value; inputting the temperature control characteristic data of the lithium battery into a second pre-configured machine learning model to obtain optimal charging data, and correcting the initial charging data of the lithium battery according to the optimal charging data; repeating the steps until Q=T+N, ending the cycle to finish the multi-time charging regulation and control of the lithium battery within the set charging duration; based on the above process, the application can predict the future temperature change of the lithium battery, and further, can determine the future state of the lithium battery according to the predicted temperature; in addition, on the premise of acquiring the predicted temperature, the optimal charging rate/optimal charging current of the lithium battery is acquired, and the charging process of the lithium battery is adaptively adjusted according to the optimal charging rate/optimal charging current, so that the safety of the charging process of the lithium battery is ensured, and meanwhile, the charging is carried out without stopping; further, the lithium battery is beneficial to ensuring that the lithium battery completes a safe charging process in a set time.
Drawings
Fig. 1 is a flowchart of a method for monitoring a charging and discharging process of a lithium battery according to the present invention;
Fig. 2 is a schematic block diagram of a monitoring system for a charging and discharging process of a lithium battery according to the present invention;
Fig. 3 is a schematic structural diagram of an electronic device according to the present invention;
fig. 4 is a schematic structural diagram of a computer readable storage medium according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the disclosure of the present embodiment provides a method for monitoring a charging and discharging process of a lithium battery, where the method includes:
s101: acquiring a set charging duration Q of a lithium battery, wherein Q is an integer greater than zero;
It should be appreciated that: in order to ensure the safety of the charging process of the lithium battery, the conventional charging strategy of the lithium battery generally adopts a constant-current charging strategy or a constant-voltage charging strategy, namely, the charging current or the charging voltage of the lithium battery is kept in a constant state in the charging process; although the constant charging strategy can ensure the charging safety of the lithium battery as much as possible, the constant charging strategy or the constant voltage charging strategy cannot cope with the charging safety problem of the lithium battery caused by other external factors due to the interference of other external factors (such as ambient temperature and the like);
It should be noted that: the set charging time length Q of the lithium battery is determined according to the residual capacity and the charging current of the lithium battery, and the charging current of the charging system is constant as can be known from the description; thus, as an exemplary illustration, it is assumed that the total capacity of the capacitor of the lithium battery is 20kWh (kilowatt-hours), the remaining capacity is 10 kWh, and the charging current is 50 amperes (a); therefore, the given charging time period Q of the lithium battery can be calculated to be 0.2 hours by the following formula:
。
S102: acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery at the T+N time, wherein the monitoring data comprises initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of the lithium battery, and N is an integer larger than zero;
Specifically, the initial charging data is an initial charging current and an initial charging rate;
Specifically, the logic for obtaining the temperature change coefficient is as follows:
extracting an ambient temperature value and a battery temperature value within a time span from T-N to T;
Carrying out formula calculation on an environment temperature value and a battery temperature value in a time span from T-N to T to obtain a temperature change coefficient; the calculation formula is as follows: ; wherein: /(I) Is the temperature change coefficient,/>Is the ambient temperature value at the ith time,/>Is the ambient temperature value at the i-1 th time,/>The battery temperature value at the i-th time,The battery temperature value at the i-1 th moment is Q, and the time span from T-N to T is Q;
It will be appreciated that: the temperature change coefficient reflects the relative rate of change between the ambient temperature and the battery temperature; the value of the coefficient can tell the change rate of the battery temperature relative to the ambient temperature, so that the coefficient can be used for predicting the estimated temperature value of the lithium battery at the moment T+N;
In an implementation, the logic for generating the first machine learning model according to the pre-configuration is as follows:
Acquiring historical temperature change data, and dividing the historical temperature change data into a predicted temperature training set and a predicted temperature testing set; the historical temperature change data comprises estimated temperature characteristic data for predicting an estimated temperature value and the corresponding estimated temperature value; the estimated temperature characteristic data comprise a plurality of groups of initial charging data of time spans, measured voltage values, environment temperature values, battery temperature values and temperature change coefficients;
The estimated temperature characteristic data in the historical temperature change data are obtained by direct acquisition or indirect calculation of various sensors, and the various sensors comprise, but are not limited to, a temperature sensor, a current sensor, a voltage sensor and a calculator;
It should be noted that: the estimated temperature value in the historical temperature change data is obtained through actual recording by technicians; the logic for acquiring the temperature change coefficient in the estimated temperature characteristic data and the temperature change coefficient in the monitoring data is not repeated for the above;
Initializing a first regression network, taking predicted temperature characteristic data in a predicted temperature training set as input data of the first regression network, taking predicted temperature values in the predicted temperature training set as output data of the first regression network, and training the first regression network to obtain an initial first machine learning network;
Performing model verification on the initial first machine learning network by using the estimated temperature test set, and outputting the initial first machine learning network with the test error less than or equal to the preset test error as a preset first machine learning model;
It should be noted that: the first regression network is specifically one of models such as decision tree regression, support vector machine regression, random forest regression, polynomial regression or neural network regression.
S103: judging whether the lithium battery at the time of T+N is in an over-temperature/low-temperature state or not according to the estimated temperature value, if not, continuously charging the lithium battery according to the initial charging data, enabling T=T+N+M, and returning to the step S101; if the temperature control characteristic data of the lithium battery appear, acquiring the temperature control characteristic data of the lithium battery, wherein M is an integer larger than zero;
In an implementation, the determining whether the over-temperature/low-temperature state occurs in the lithium battery at the t+n time includes:
Acquiring a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 of the lithium battery, wherein Td1 is more than Td2;
Comparing the estimated temperature value with a preset upper temperature threshold Td1, and if the estimated temperature value is larger than Td1, judging that the lithium battery at the time of T+N is in an over-temperature state; if the estimated temperature value is smaller than or equal to Td1, comparing the estimated temperature value with a preset temperature lower limit threshold Td 2; if the estimated temperature value is greater than or equal to Td2, judging that the lithium battery at the moment T+N does not have an over-temperature/low-temperature state; if the estimated temperature value is smaller than Td2, judging that the lithium battery at the moment T+N is in a low-temperature state;
It should be understood that: when the estimated temperature value is greater than Td1, that is, when the lithium battery is in an over-temperature state, the situation that the temperature of the battery of the lithium battery is too high at a certain future time point after the lithium battery is charged according to an original charging strategy (that is, constant charging current) is indicated; in contrast, when the estimated temperature value is smaller than Td2, i.e., when the lithium battery is in a low-temperature state, it is indicated that the battery temperature of the lithium battery is too low at a certain future point in time after the lithium battery is charged according to the original charging strategy (i.e., constant charging current); it is further understood that the lithium battery is not good for charging protection when in an over-temperature/low-temperature state; therefore, by decreasing the initial charge current or the initial charge rate of the lithium battery in the over-temperature state, the initial charge current or the initial charge rate of the lithium battery is increased in the low-temperature state; the charging safety of the lithium battery is met, and the charging speed of the lithium battery can be ensured;
Specifically, the temperature control characteristic data of the lithium battery includes a first temperature difference and a second temperature difference, wherein the first temperature difference is a difference between a predicted temperature value and a preset upper temperature threshold Td1, and the second temperature difference is a difference between a predicted temperature value and a preset lower temperature threshold Td 2.
S104: inputting temperature control characteristic data of the lithium battery into a second preconfigured machine learning model to obtain optimal charging data, correcting initial charging data of the lithium battery according to the optimal charging data, and charging the lithium battery according to the corrected initial charging data within a time range from T to T+N;
specifically, the optimal charging data is a specific one of an optimal charging current or an optimal charging rate;
specifically, the second preconfigured machine learning model is generated according to lithium battery test data in a training mode, wherein the lithium battery test data at least comprises one of the relation between the optimal charging current and the temperature control characteristic data or the relation between the optimal charging rate and the temperature control characteristic data;
In implementation, the generation logic of the preconfigured second machine learning model is as follows:
Acquiring historical charging test data of the lithium battery, and dividing the historical charging test data into an optimal charging training set and an optimal charging test set; the historical charging test data comprises temperature control characteristic data and corresponding optimal charging current or optimal charging rate;
It should be noted that: the temperature control characteristic data in the historical charging test data are recorded in advance by a technician, namely, the acquired estimated temperature value is respectively subjected to difference calculation with a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, and the historical charging test data are obtained by carrying out relation binding on the first temperature difference and the second temperature difference with the optimal charging current or carrying out relation binding on the first temperature difference and the second temperature difference with the optimal charging rate;
in one specific embodiment, the logic for generating the relationship between the optimal charging current and the temperature control characteristic data is:
Step a1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step a2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging current R in initial charging data, enabling R=R+1 or R=R-1, and returning to the step a1, wherein R is an integer larger than zero;
Step a3: repeating the steps a1 to a2 until the first temperature difference or the second temperature difference is equal to the set first threshold value, ending the cycle, and taking the charging current corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
Step a4: binding the relation between the first temperature difference or the second temperature difference and the optimal charging current to obtain the relation between the optimal charging current and the temperature control characteristic data;
In one specific embodiment, the generating logic of the relation between the optimal charging rate and the temperature control characteristic data is as follows:
Step b1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step b2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging rate V in initial charging data, enabling V=V+1 or V=V-1, and returning to the step b1, wherein V is an integer larger than zero;
Step b3: repeating the steps b1 to b2 until the first temperature difference or the second temperature difference is equal to the set second threshold value, ending the cycle, and taking the charging rate corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
It should be understood that: setting a first threshold value, wherein the value of the second threshold value is zero; it can be understood that when the first temperature difference or the second temperature difference is equal, the estimated temperature value of the lithium battery is determined to be in a normal range, i.e. the lithium battery is not in an over-temperature/low-temperature state, so that the charging current corresponding to the time is taken as the optimal charging current, or the charging rate corresponding to the time is taken as the optimal charging rate, and the charging process of the lithium battery is controlled according to the optimal charging current or the optimal charging rate, so that the lithium battery is ensured not to have a safety problem in future time, and the charging speed of the lithium battery is ensured;
step b4: carrying out relation binding on the first temperature difference or the second temperature difference and the optimal charging rate to obtain the relation between the optimal charging rate and temperature control characteristic data;
Initializing a second regression network, taking temperature control characteristic data in an optimal charging training set as input data of the second regression network, taking optimal charging current or optimal charging rate in the optimal charging training set as output data of the second regression network, and training the second regression network to obtain an initial second machine learning network;
performing model verification on the initial second machine learning network by using the optimal charging test set, and outputting the initial second machine learning network with the test error less than or equal to the preset test error as a preconfigured second machine learning model;
It should be noted that: the second regression network is a specific one of models such as decision tree regression, support vector machine regression, random forest regression, polynomial regression or neural network regression;
It will be appreciated that: when the optimal charging current or the optimal charging rate is obtained, the initial charging data of the lithium battery is corrected according to the optimal charging data, so that the lithium battery can be ensured not to be in an over-temperature/low-temperature state in the charging process; by way of example, assuming an initial charge current of 50 amps and an optimal charge current of 70 amps, adjusting the initial charge current from 50 amps to 70 amps may accomplish a correction to the initial charge data for the lithium battery; the same is true for the logic for correcting the initial charge rate, which will not be repeated.
S105: repeating the steps S102-S104 until Q=T+N, ending the cycle to finish the multi-time charging regulation and control of the lithium battery within the set charging duration;
It will be appreciated that: the temperature of the lithium battery in the charging process can be adjusted by controlling the initial charging current or the initial charging rate; therefore, when the situation that whether the lithium battery is in an over-temperature/low-temperature state is predicted, the initial charging current or the initial charging rate of the lithium battery is dynamically adjusted according to the acquired optimal charging data of the lithium battery, so that intelligent temperature control of the lithium battery can be realized, and the charging speed of the lithium battery can be ensured not to be reduced due to changing of the charging current or the charging rate while the safety of the lithium battery in the charging process is ensured.
Example 2
Referring to fig. 2, based on the same inventive concept, the disclosure of this embodiment provides a monitoring system for a charging and discharging process of a lithium battery, including:
The data acquisition module 210 is configured to acquire a predetermined charging duration Q of the lithium battery, where Q is an integer greater than zero.
The temperature prediction module 220 is configured to obtain monitoring data of the lithium battery from T-N to T, input the monitoring data into a preconfigured first machine learning model, predict an estimated temperature value of the lithium battery at t+n, where the monitoring data includes initial charging data, an actually measured voltage value, an ambient temperature value, a battery temperature value, and a temperature change coefficient of the lithium battery, and N is an integer greater than zero;
Specifically, the initial charging data is an initial charging current and an initial charging rate;
Specifically, the logic for obtaining the temperature change coefficient is as follows:
extracting an ambient temperature value and a battery temperature value within a time span from T-N to T;
Carrying out formula calculation on an environment temperature value and a battery temperature value in a time span from T-N to T to obtain a temperature change coefficient; the calculation formula is as follows: ; wherein: /(I) Is the temperature change coefficient,/>Is the ambient temperature value at the ith time,/>Is the ambient temperature value at the i-1 th time,/>The battery temperature value at the i-th time,The battery temperature value at the i-1 th moment is Q, and the time span from T-N to T is Q;
In an implementation, the logic for generating the first machine learning model according to the pre-configuration is as follows:
Acquiring historical temperature change data, and dividing the historical temperature change data into a predicted temperature training set and a predicted temperature testing set; the historical temperature change data comprises estimated temperature characteristic data for predicting an estimated temperature value and the corresponding estimated temperature value; the estimated temperature characteristic data comprise a plurality of groups of initial charging data of time spans, measured voltage values, environment temperature values, battery temperature values and temperature change coefficients;
The estimated temperature characteristic data in the historical temperature change data are obtained by direct acquisition or indirect calculation of various sensors, and the various sensors comprise, but are not limited to, a temperature sensor, a current sensor, a voltage sensor and a calculator;
Initializing a first regression network, taking predicted temperature characteristic data in a predicted temperature training set as input data of the first regression network, taking predicted temperature values in the predicted temperature training set as output data of the first regression network, and training the first regression network to obtain an initial first machine learning network;
And performing model verification on the initial first machine learning network by using the estimated temperature test set, and outputting the initial first machine learning network with the test error less than or equal to the preset test error as a preset first machine learning model.
The state judging module 230 is configured to judge whether an over-temperature/low-temperature state occurs in the lithium battery at the time t+n according to the estimated temperature value, if not, continue to charge the lithium battery according to the initial charging data, and let t=t+n+m, and trigger the data acquiring module 210; if the temperature control characteristic data of the lithium battery appear, acquiring the temperature control characteristic data of the lithium battery, wherein M is an integer larger than zero;
In an implementation, the determining whether the over-temperature/low-temperature state occurs in the lithium battery at the t+n time includes:
Acquiring a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 of the lithium battery, wherein Td1 is more than Td2;
Comparing the estimated temperature value with a preset upper temperature threshold Td1, and if the estimated temperature value is larger than Td1, judging that the lithium battery at the time of T+N is in an over-temperature state; if the estimated temperature value is smaller than or equal to Td1, comparing the estimated temperature value with a preset temperature lower limit threshold Td 2; if the estimated temperature value is greater than or equal to Td2, judging that the lithium battery at the moment T+N does not have an over-temperature/low-temperature state; if the estimated temperature value is smaller than Td2, judging that the lithium battery at the moment T+N is in a low-temperature state;
Specifically, the temperature control characteristic data of the lithium battery includes a first temperature difference and a second temperature difference, wherein the first temperature difference is a difference between a predicted temperature value and a preset upper temperature threshold Td1, and the second temperature difference is a difference between a predicted temperature value and a preset lower temperature threshold Td 2.
The parameter correction module 240 is configured to input temperature control feature data of the lithium battery into a second machine learning model to obtain optimal charging data, correct initial charging data of the lithium battery according to the optimal charging data, and charge the lithium battery according to the corrected initial charging data within a time range from T to t+n;
specifically, the optimal charging data is a specific one of an optimal charging current or an optimal charging rate;
specifically, the second preconfigured machine learning model is generated according to lithium battery test data in a training mode, wherein the lithium battery test data at least comprises one of the relation between the optimal charging current and the temperature control characteristic data or the relation between the optimal charging rate and the temperature control characteristic data;
In implementation, the generation logic of the preconfigured second machine learning model is as follows:
Acquiring historical charging test data of the lithium battery, and dividing the historical charging test data into an optimal charging training set and an optimal charging test set; the historical charging test data comprises temperature control characteristic data and corresponding optimal charging current or optimal charging rate;
in one specific embodiment, the logic for generating the relationship between the optimal charging current and the temperature control characteristic data is:
Step a1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step a2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging current R in initial charging data, enabling R=R+1 or R=R-1, and returning to the step a1, wherein R is an integer larger than zero;
Step a3: repeating the steps a1 to a2 until the first temperature difference or the second temperature difference is equal to the set first threshold value, ending the cycle, and taking the charging current corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
Step a4: binding the relation between the first temperature difference or the second temperature difference and the optimal charging current to obtain the relation between the optimal charging current and the temperature control characteristic data;
In one specific embodiment, the generating logic of the relation between the optimal charging rate and the temperature control characteristic data is as follows:
Step b1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step b2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging rate V in initial charging data, enabling V=V+1 or V=V-1, and returning to the step b1, wherein V is an integer larger than zero;
Step b3: repeating the steps b1 to b2 until the first temperature difference or the second temperature difference is equal to the set second threshold value, ending the cycle, and taking the charging rate corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
step b4: carrying out relation binding on the first temperature difference or the second temperature difference and the optimal charging rate to obtain the relation between the optimal charging rate and temperature control characteristic data;
Initializing a second regression network, taking temperature control characteristic data in an optimal charging training set as input data of the second regression network, taking optimal charging current or optimal charging rate in the optimal charging training set as output data of the second regression network, and training the second regression network to obtain an initial second machine learning network;
And performing model verification on the initial second machine learning network by using the optimal charging test set, and outputting the initial second machine learning network with the test error less than or equal to the preset test error as a preconfigured second machine learning model.
The adaptive control module 250 is configured to repeat the above steps from the temperature prediction module 220 to the parameter correction module 240 until q=t+n, and end the cycle, so as to complete the multiple charging control of the lithium battery within the predetermined charging duration.
Example 3
Referring to fig. 3, the disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and running on the processor, where the processor implements any one of the above methods when executing the computer program.
Since the electronic device described in this embodiment is an electronic device used to implement the method for monitoring the charging and discharging process of a lithium battery in this embodiment, based on the method for monitoring the charging and discharging process of a lithium battery described in this embodiment, those skilled in the art can understand the specific implementation of the electronic device and various modifications thereof, so how to implement the method in this embodiment of the application for this electronic device will not be described in detail herein. As long as the person skilled in the art implements the electronic device adopted by the method for monitoring the charging and discharging process of the lithium battery in the embodiment of the application, the electronic device belongs to the scope of protection required by the application.
Example 4
Referring to fig. 4, the disclosure provides a computer readable storage medium, which includes a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor implements any one of the above methods when executing the computer program.
The above formulas are all formulas with dimensionality removed and numerical value calculated, the formulas are formulas with the latest real situation obtained by software simulation by collecting a large amount of data, and preset parameters, weights and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A method for monitoring a charging and discharging process of a lithium battery, the method comprising:
s101: acquiring a set charging duration Q of a lithium battery, wherein Q is an integer greater than zero;
S102: acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery at the T+N time, wherein the monitoring data comprises initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of the lithium battery, and N is an integer larger than zero;
S103: judging whether the temperature of the lithium battery at the time of T+N is abnormal or not according to the estimated temperature value, if not, continuously charging the lithium battery according to the initial charging data, enabling T=T+N+M, and returning to the step S101; if the temperature control characteristic data of the lithium battery appear, acquiring the temperature control characteristic data of the lithium battery, wherein M is an integer larger than zero;
S104: inputting temperature control characteristic data of the lithium battery into a second preconfigured machine learning model to obtain optimal charging data, correcting initial charging data of the lithium battery according to the optimal charging data, and charging the lithium battery according to the corrected initial charging data within a time range from T to T+N;
S105: repeating the steps S102-S104 until Q=T+N, ending the cycle to complete the multi-time charging regulation and control of the lithium battery within the set charging duration.
2. The method for monitoring a charge and discharge process of a lithium battery according to claim 1, wherein the initial charge data is an initial charge current and an initial charge rate;
the acquisition logic of the temperature change coefficient is as follows:
extracting an ambient temperature value and a battery temperature value within a time span from T-N to T;
Carrying out formula calculation on an environment temperature value and a battery temperature value in a time span from T-N to T to obtain a temperature change coefficient; the calculation formula is as follows: ; wherein: /(I) Is the temperature change coefficient,/>Is the ambient temperature value at the ith time,/>Is the ambient temperature value at the i-1 th time,/>The battery temperature value at the i-th time,And Q is the time span from T-N to T for the battery temperature value at the i-1 th moment.
3. The method for monitoring a charging and discharging process of a lithium battery according to claim 2, wherein the generating logic of the preconfigured first machine learning model is as follows:
Acquiring historical temperature change data, and dividing the historical temperature change data into a predicted temperature training set and a predicted temperature testing set; the historical temperature change data comprises estimated temperature characteristic data for predicting an estimated temperature value and the corresponding estimated temperature value; the estimated temperature characteristic data comprise a plurality of groups of initial charging data of time spans, measured voltage values, environment temperature values, battery temperature values and temperature change coefficients;
Initializing a first regression network, taking predicted temperature characteristic data in a predicted temperature training set as input data of the first regression network, taking predicted temperature values in the predicted temperature training set as output data of the first regression network, and training the first regression network to obtain an initial first machine learning network;
And performing model verification on the initial first machine learning network by using the estimated temperature test set, and outputting the initial first machine learning network with the test error less than or equal to the preset test error as a preset first machine learning model.
4. A method for monitoring a charge and discharge process of a lithium battery according to claim 3, wherein the temperature abnormality includes an over-temperature state and a low-temperature state;
The judging whether the temperature abnormality occurs in the lithium battery at the time of T+N comprises the following steps:
Acquiring a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 of the lithium battery, wherein Td1 is more than Td2;
Comparing the estimated temperature value with a preset upper temperature threshold Td1, and if the estimated temperature value is larger than Td1, judging that the lithium battery at the time of T+N is in an over-temperature state; if the estimated temperature value is smaller than or equal to Td1, comparing the estimated temperature value with a preset temperature lower limit threshold Td 2; if the estimated temperature value is greater than or equal to Td2, judging that the lithium battery at the moment T+N has no temperature abnormality; if the estimated temperature value is smaller than Td2, the lithium battery at the time of T+N is judged to be in a low-temperature state.
5. The method for monitoring a charging and discharging process of a lithium battery according to claim 4, wherein the temperature control characteristic data of the lithium battery comprises a first temperature difference and a second temperature difference, the first temperature difference is a difference value between a predicted temperature value and a preset upper temperature threshold Td1, and the second temperature difference is a difference value between a predicted temperature value and a preset lower temperature threshold Td 2;
the second machine learning model is generated according to training of lithium battery test data, and the lithium battery test data at least comprises one of the relation between the optimal charging current and the temperature control characteristic data or the relation between the optimal charging rate and the temperature control characteristic data.
6. The method for monitoring a charging and discharging process of a lithium battery according to claim 5, wherein the generation logic of the preconfigured second machine learning model is as follows:
Acquiring historical charging test data of the lithium battery, and dividing the historical charging test data into an optimal charging training set and an optimal charging test set; the historical charging test data comprises temperature control characteristic data and corresponding optimal charging current or optimal charging rate;
Initializing a second regression network, taking temperature control characteristic data in an optimal charging training set as input data of the second regression network, taking optimal charging current or optimal charging rate in the optimal charging training set as output data of the second regression network, and training the second regression network to obtain an initial second machine learning network;
And performing model verification on the initial second machine learning network by using the optimal charging test set, and outputting the initial second machine learning network with the test error less than or equal to the preset test error as a preconfigured second machine learning model.
7. The method for monitoring a charging and discharging process of a lithium battery according to claim 6, wherein the generating logic of the relation between the optimal charging current and the temperature control characteristic data is as follows:
Step a1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step a2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging current R in initial charging data, enabling R=R+1 or R=R-1, and returning to the step a1, wherein R is an integer larger than zero;
Step a3: repeating the steps a1 to a2 until the first temperature difference or the second temperature difference is equal to the set first threshold value, ending the cycle, and taking the charging current corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
Step a4: binding the relation between the first temperature difference or the second temperature difference and the optimal charging current to obtain the relation between the optimal charging current and the temperature control characteristic data;
The generation logic of the relation between the optimal charging rate and the temperature control characteristic data is as follows:
Step b1: in a test scene, inputting initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of a test lithium battery at the time T-N to T into a preconfigured first machine learning model, and obtaining an estimated temperature value of the test lithium battery at the time T+N;
Step b2: respectively carrying out difference calculation on the estimated temperature value and a preset upper temperature threshold Td1 and a preset lower temperature threshold Td2 to obtain a first temperature difference and a second temperature difference, reading an initial charging rate V in initial charging data, enabling V=V+1 or V=V-1, and returning to the step b1, wherein V is an integer larger than zero;
Step b3: repeating the steps b1 to b2 until the first temperature difference or the second temperature difference is equal to the set second threshold value, ending the cycle, and taking the charging rate corresponding to the first temperature difference or the second temperature difference is equal to the set threshold value as the optimal charging current;
step b4: and carrying out relation binding on the first temperature difference or the second temperature difference and the optimal charging rate to obtain the relation between the optimal charging rate and the temperature control characteristic data.
8. A lithium battery charge-discharge process monitoring system, comprising:
the data acquisition module is used for acquiring the set charging time length Q of the lithium battery, wherein Q is an integer greater than zero;
The temperature prediction module is used for acquiring monitoring data of the lithium battery from the T-N time to the T time, inputting the monitoring data into a preconfigured first machine learning model, and predicting an estimated temperature value of the lithium battery at the T+N time, wherein the monitoring data comprises initial charging data, an actually measured voltage value, an environment temperature value, a battery temperature value and a temperature change coefficient of the lithium battery, and N is an integer larger than zero;
The state judging module is used for judging whether the temperature of the lithium battery at the time of T+N is abnormal or not according to the estimated temperature value, if not, continuously charging the lithium battery according to the initial charging data, enabling T=T+N+M, and triggering the data acquisition module; if the temperature control characteristic data of the lithium battery appear, acquiring the temperature control characteristic data of the lithium battery, wherein M is an integer larger than zero;
The parameter correction module is used for inputting the temperature control characteristic data of the lithium battery into a second preconfigured machine learning model to obtain optimal charging data, correcting the initial charging data of the lithium battery according to the optimal charging data, and charging the lithium battery according to the corrected initial charging data within the time range from T to T+N;
And the self-adaptive control module is used for repeating the temperature prediction module to the parameter correction module until Q=T+N, and ending the cycle so as to finish the multi-time charging regulation and control of the lithium battery within the set charging duration.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements a lithium battery charging and discharging process monitoring method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when executed, the computer program implements a lithium battery charging and discharging process monitoring method according to any one of claims 1 to 7.
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Citations (5)
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| CN117293975A (en) * | 2023-11-22 | 2023-12-26 | 云南华尔贝光电技术有限公司 | Charging and discharging adjustment method, device and equipment for lithium battery and storage medium |
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| CN109412250A (en) * | 2018-10-16 | 2019-03-01 | 河海大学常州校区 | A kind of determination method of off-network electricity generation system energy-storage battery optimal charge rate |
| US10958211B1 (en) * | 2019-10-04 | 2021-03-23 | The Florida International University Board Of Trustees | Systems and methods for power management |
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