CN114814588B - Method for rapidly estimating capacity of storage battery - Google Patents
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention discloses a method for rapidly estimating the capacity of a storage battery, which comprises three core algorithms: the method comprises the steps of obtaining a voltage standard value through big data analysis, judging whether the storage battery capacity meets the standard or not and predicting the storage battery capacity. Through comprehensive analysis and test of the discharge characteristics of the storage battery, a set of unique calculation method suitable for intelligent analysis of a computer is constructed to replace the traditional storage battery capacity assessment test method; meanwhile, the system integrating the new algorithm overcomes the defect of the traditional monitoring technology, has the function of measuring the capacity of each battery in the storage battery, enables the operation and maintenance unit to adopt different maintenance strategies according to the actual condition of each battery in the battery pack, reduces the elimination rate of the battery, prolongs the service life of the storage battery, saves the operation cost of the production unit, and solves the problems in the prior art.
Description
Technical Field
The invention belongs to the technical field of a rapid estimation method of storage battery capacity, and particularly relates to a rapid estimation method of storage battery capacity.
Background
The storage battery is widely used as a standby power supply of important facility equipment such as communication, control mechanisms, electronic calculation, network information and the like in the industrial production fields such as rail transit, chemical industry, electric power, new energy, finance, communication and the like, and plays an important role in ensuring that an important business system keeps normal operation under the condition that an external power supply fails or the quality of the power supply is reduced; as an important energy storage device, a storage battery works by converting chemical energy in a body into electric energy, and rapid judgment of the actual capacity of the storage battery is always a pain point and a difficulty in the industry. But with the wide application of the online monitoring system of the storage battery, the online monitoring system of the storage battery has the condition of rapidly estimating the capacity of the storage battery according to a special algorithm.
The existing storage battery capacity checking method is that the storage battery is discharged according to a specified constant current according to a capacity checking standard of the storage battery, the capacity of the storage battery pack is judged according to the time taken before the voltage drops to a cut-off voltage, namely, the actual discharging time is recorded to determine the capacity of the storage battery; the traditional storage battery capacity checking method is long in time consumption, 80% and 100% capacity checking needs 8 hours and 10 hours to finally confirm whether the capacity of the storage battery is qualified or not, and production efficiency is low, and for chemical industry, finance, communication and rail transportation enterprises, in order to ensure production safety, the operation time is usually only 3-5 hours, and the requirements of the traditional detection process cannot be met.
On the other hand, in the conventional capacity checking method, when the voltage of one battery in the storage battery pack is reduced to the cut-off voltage, discharging is stopped, and if the discharging time period does not reach the specified standard, the whole battery pack is judged to be not up to standard. In practice, after a period of operation, the performance of each cell in the battery pack will be reduced in different degrees, and even there will be a large performance difference between samples. The traditional capacity assessment method only makes a judgment according to the battery with the worst performance in the battery pack, does not have the capability of judging the actual performance of each battery in the battery pack, cannot screen according to the quality of the battery performance, and can cause waste of a large number of benign batteries.
Disclosure of Invention
The invention aims to provide a rapid estimation method for the capacity of a storage battery, which constructs a set of unique calculation method suitable for intelligent computer analysis through comprehensive analysis and test of the discharge characteristic of the storage battery to replace the traditional storage battery capacity assessment test method. The online storage battery monitoring system integrating the new algorithm can rapidly measure the capacity of the storage battery within 3 hours of discharge test, solves the problem that enterprises such as chemical industry, finance, communication, rail traffic and the like cannot carry out capacity assessment test on the storage battery pack according to the traditional process method, and solves the problem in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme: a method for rapidly estimating the capacity of a storage battery comprises three core algorithms: an algorithm for obtaining a voltage standard value through big data analysis, an algorithm for judging whether the capacity of the storage battery meets the standard or not and an algorithm for predicting the capacity of the storage battery;
the algorithm for obtaining the voltage standard value through big data analysis comprises the following steps: according to a storage battery capacity check test, respectively screening storage batteries with the capacity of 50-100% of rated capacity according to the same capacity as a standard, and drawing a voltage distribution diagram of the storage batteries in the capacity check for 3 hours;
By collecting the storage battery capacity check data, the voltage value of the storage battery with the same capacity at a certain moment is taken as a set, a sample of the set accords with normal distribution characteristics, an expected value mu is an average value of the sample, the position of the sample is determined, the standard deviation sigma determines the distribution amplitude of the sample, and the relation between the standard deviation sigma and the sample x i is shown in the expression 1: according to the probability distribution relation of a normal curve, the area in a horizontal axis interval (mu-1.96 delta, mu+1.96 delta) is 95.449974%, so that a voltage standard value U z = mu-1.96 delta corresponding to a certain capacity C is obtained through big data analysis;
The algorithm for judging whether the capacity of the storage battery meets the standard comprises the following steps: the method comprises the steps of randomly extracting two groups of 36 storage batteries to perform capacity check tests to obtain a storage battery discharging curve, finding out the discharging curves of the storage batteries with different capacities, wherein the capacities of the storage batteries with different capacities are related to voltage and voltage change rate, rated capacities are the same, and the voltages of the storage batteries with different actual capacities are lower along with the smaller capacity, so that the voltage of the discharging curve is reduced faster; according to the correlation, firstly, a voltage standard value U z is obtained, and then according to the voltage standard value, the corresponding relation between the voltage and the time which can be met if the capacity of any storage battery needs to be achieved can be obtained;
The voltage standard value corresponding to the storage battery with the capacity of C at the time t 2 is U z, and assuming that the voltage value of a certain battery is measured at the time t 1, the condition that the battery reaches the capacity of C is that the voltage value U x of any time t x of t 1～t2 must be greater than U xz, and the corresponding relation is as shown in the expression 2:
the algorithm for predicting the capacity of the storage battery comprises the following steps: obtaining a voltage standard value U zi corresponding to the capacity C i of a certain type of storage battery at a certain time t i by utilizing big data analysis, wherein i is an integer larger than 0;
Secondly, according to an algorithm for judging whether the capacity of the storage battery reaches the standard, measuring a voltage U at a time t 1 of capacity check, obtaining voltage thresholds U x1、Ux2 corresponding to the capacities of C 1、C2 respectively according to an expression 2, and judging that the capacity of the storage battery is C 2 when the voltage U x measured at the time t x meets an expression U x2＜Ux＜Ux1;
the method for realizing the rapid estimation of the capacity of the storage battery according to the three core algorithms comprises the following steps:
Step one, obtaining voltage standard values corresponding to different capacities through big data analysis, wherein the standard values are used as key parameters for judging whether the capacity of the storage battery meets the standard or not;
Step two, after obtaining the voltage standard value, according to the algorithm for judging whether the capacity of the storage battery meets the standard, we can calculate the voltage distribution rule meeting the current capacity, so that the voltage sampling value at any moment can be compared with the rule to judge whether the capacity of the storage battery meets the standard;
And thirdly, sorting the voltage standard values of the storage battery according to the corresponding capacities, sequentially comparing the voltage sampling values at any time with the sorted judgment standard, if the voltage sampling values are judged to be up to standard, the capacity of the storage battery is the capacity corresponding to the judgment standard, and if the voltage sampling values are judged to be not up to standard, selecting the judgment standard with smaller capacity for comparison, so that the capacity of the storage battery is obtained.
Preferably, in the algorithm for obtaining the voltage standard value through big data analysis, the storage battery pack comprises storage batteries with the same rated capacity, the same rated voltage, the same operation duration and different performances.
Preferably, the estimated battery pack may include six capacity batteries: a battery having a rated capacity of 100% and a battery having a rated capacity of 90% and a battery having a rated capacity of 80% and a battery having a rated capacity of 70% and a battery having a rated capacity of 60% and a battery having a rated capacity of 50%.
Preferably, the storage battery capacity checking voltage hash map has the following rules: the voltage distribution of the storage batteries with different capacities is discrete at the same moment, and the voltage distribution of the storage batteries with the same capacities at the same moment is relatively concentrated.
Preferably, the capacity of the battery can be estimated from the voltage values according to the law.
Preferably, the system also comprises a storage battery on-line monitoring system, wherein the storage battery on-line monitoring system comprises a back-end device, a sensor module, a data aggregation module, a wireless transmission module and a monitoring server/comprehensive monitoring platform.
Preferably, the sensor module is used for detecting parameters such as battery voltage, current and the like, and sending data through an internal bus.
Preferably, the data convergence module is used for realizing the signal conversion function of the communication port of the sensor module and the back-end equipment RS485 or TCP/IP.
Preferably, the wireless transmission module is used for transmitting the data through wireless communication by using a wireless internet of things card.
Preferably, the monitoring server/comprehensive monitoring platform integrates a rapid estimation method of the capacity of the storage battery, automatically calculates the current state according to the collected voltage parameters of the storage battery, and performs visual processing on the data.
The invention has the technical effects and advantages that: compared with the prior art, the method for rapidly estimating the capacity of the storage battery has the following advantages:
A set of unique calculation method suitable for computer intelligent analysis is constructed through comprehensive analysis and test of the discharge characteristics of the storage battery, and replaces the traditional storage battery capacity assessment test method. The online storage battery monitoring system integrating the new algorithm can rapidly measure the capacity of the storage battery within 3 hours of discharge test, so that the problem that enterprises such as chemical industry, finance, communication, rail traffic and the like cannot perform capacity assessment test on the storage battery pack according to the traditional process method is solved; the system integrated with the new algorithm overcomes the defects of the traditional monitoring process, has the function of measuring the capacity of each battery in the storage battery, enables the operation and maintenance unit to adopt different maintenance strategies according to the actual condition of each battery in the battery pack, reduces the elimination rate of the battery, prolongs the service life of the storage battery, saves the operation cost of the production unit, and solves the problems in the prior art
The invention solves the problem that the maintenance operation time of the industries such as rail transit and the like is insufficient, and the capacity assessment test can not be carried out on the storage battery pack according to the traditional maintenance process, and can rapidly determine the actual capacity of the storage battery, so that an operation and maintenance person can timely find the unqualified storage battery, the risk of system failure caused by the failure of the storage battery is reduced, and the safety coefficient of system operation is improved. And secondly, the technical defect that the traditional storage battery capacity assessment method only can evaluate the whole battery pack is overcome, the performance of each battery pack participating in assessment can be evaluated, operation and maintenance personnel are allowed to adopt corresponding strategies to process according to different operation conditions of the storage battery pack, the rejection rate of the storage battery is reduced, the service life of the storage battery is prolonged, the operation cost is saved, and finally, the workload of the operation and maintenance personnel is reduced due to the fact that the detection time is shortened.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
FIG. 1 is a voltage hash diagram of the battery pack obtained by a capacity check test of the battery pack under the same check time period;
FIG. 2 is a graph showing the voltage distribution of 28 full-capacity batteries in the invention after 3 hours of examination;
FIG. 3 is a normal distribution curve surface integral layout of a capacity check test according to the present invention;
FIG. 4 is a graph showing an example of the voltage trend of the capacity check test according to the present invention;
FIG. 5 is a schematic diagram of an on-line battery monitoring system according to the present invention;
FIG. 6 is a graph showing the relationship between voltage and time corresponding to the predicted battery capacity C according to the present invention;
FIG. 7 is a schematic diagram of an algorithm for predicting battery capacity in accordance with the present invention;
FIG. 8 is a visual diagram of the data of the fast estimation model of the battery capacity of the present invention;
FIG. 9 is a second visual chart of the fast battery capacity estimation model data according to the present invention;
FIG. 10 is a visual diagram of the data of the fast battery capacity estimation model according to the present invention;
FIG. 11 is a visual diagram of the data of the fast battery capacity estimation model according to the present invention;
FIG. 12 is a flowchart of an implementation of a computer program algorithm 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. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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.
The present invention provides embodiments as shown in fig. 1-12:
a method for rapidly estimating the capacity of a storage battery comprises three core algorithms: the principle of the algorithm for acquiring the voltage standard value through big data analysis, the algorithm for judging whether the storage battery capacity meets the standard or not and the algorithm for predicting the storage battery capacity is as follows:
The voltage hash diagrams of the storage battery packs with the same rated capacity, the same rated voltage, the same operation time and different performances (in fig. 4, the capacity of the 1# battery to the 5# battery is 100% of the rated capacity, and the capacity of the 6# battery to the 10# battery is 70% of the rated capacity) after 3 hours of capacity check are as follows: 1) The storage batteries with different capacities are discrete in voltage distribution at the same moment; 2) The voltage distribution of the same capacity batteries at the same time is relatively concentrated. According to the two rules, the capacity of the storage battery can be estimated through the voltage value.
The algorithm for obtaining the voltage standard value through big data analysis comprises the following steps: randomly extracting two groups of 36 storage batteries in total to perform a capacity check test, screening out 28 storage batteries with the capacity of 100% of rated capacity, and drawing a voltage distribution diagram of the storage batteries when the storage batteries are checked for 3 hours;
fig. 2 shows that the voltage value was 1 time between 12 and 12.2V inclusive, 7 times between 12.2 and 12.4V inclusive, 8 times between 12.4 and 12.6V inclusive, 8 times between 12.6 and 12.8V inclusive, and 4 times between 12.8 and 13V inclusive, and the drawn pattern conforms to the feature of normal distribution.
By collecting the storage battery capacity check data, the voltage value of the storage battery with the same capacity at a certain moment is taken as a set, a sample of the set accords with normal distribution characteristics, an expected value mu is an average value of the sample, the position of the sample is determined, the standard deviation sigma determines the distribution amplitude of the sample, and the relation between the standard deviation sigma and the sample x i is shown in the expression 1: As shown in the probability distribution relation (fig. 3) of the normal curve, the area in the horizontal axis interval (μ -1.96 δ, μ+1.96 δ) is 95.449974%, so that the voltage standard value U z =μ -1.96 σ corresponding to a certain capacity C is obtained by large data analysis;
The algorithm for judging whether the capacity of the storage battery meets the standard comprises the following steps: the method comprises the steps of carrying out capacity check tests by randomly extracting two groups of 36 batteries to obtain a battery discharging curve, finding out the discharging curves of the batteries with different capacities, wherein the capacities of the batteries with different capacities are related to the voltage and the voltage change rate, and as shown in fig. 4, the rated capacities of the batteries with the same actual capacities are the same, and the voltage of the discharging curve is lower along with the smaller capacity, and the voltage is reduced faster; according to the correlation, firstly, a voltage standard value U z is obtained, and then according to the voltage standard value, the corresponding relation between the voltage and the time which can be met if the capacity of any storage battery needs to be achieved can be obtained;
The voltage standard value corresponding to the storage battery with the capacity of C at the time t 2 is U z, and assuming that the voltage value of a certain battery is measured at the time t 1, the condition that the battery reaches the capacity of C is that the voltage value U x of any time t x of t 1～t2 must be greater than U xz, and the corresponding relation is as shown in the expression 2:
the algorithm for predicting the capacity of the storage battery comprises the following steps: obtaining a voltage standard value U zi corresponding to the capacity C i of a certain type of storage battery at a certain time t i by utilizing big data analysis, wherein i is an integer larger than 0;
Secondly, according to an algorithm for judging whether the capacity of the storage battery reaches the standard, measuring a voltage U at a time t 1 of capacity check, obtaining voltage thresholds U x1、Ux2 corresponding to the capacities of C 1、C2 respectively according to an expression 2, and judging that the capacity of the storage battery is C 2 when the voltage U x measured at the time t x meets an expression U x2＜Ux＜Ux1;
the method for realizing the rapid estimation of the capacity of the storage battery according to the three core algorithms comprises the following steps:
Step one, obtaining voltage standard values corresponding to different capacities through big data analysis, wherein the standard values are used as key parameters for judging whether the capacity of the storage battery meets the standard or not;
Step two, after obtaining the voltage standard value, according to the algorithm for judging whether the capacity of the storage battery meets the standard, we can calculate the voltage distribution rule meeting the current capacity, so that the voltage sampling value at any moment can be compared with the rule to judge whether the capacity of the storage battery meets the standard;
And thirdly, sorting the voltage standard values of the storage battery according to the corresponding capacities, sequentially comparing the voltage sampling values at any time with the sorted judgment standard, if the voltage sampling values are judged to be up to standard, the capacity of the storage battery is the capacity corresponding to the judgment standard, and if the voltage sampling values are judged to be not up to standard, selecting the judgment standard with smaller capacity for comparison, so that the capacity of the storage battery is obtained.
In the algorithm for obtaining the voltage standard value through big data analysis, the storage battery pack comprises storage batteries with the same rated capacity, the same rated voltage, the same operation duration and different performances; the estimated battery pack may include six capacities of batteries: a battery having a rated capacity of 100% and a battery having a rated capacity of 90% and a battery having a rated capacity of 80% and a battery having a rated capacity of 70% and a battery having a rated capacity of 60% and a battery having a rated capacity of 50%.
The voltage hash graph for the storage battery capacity check has the following rules: the voltage distribution of the storage batteries with different capacities at the same moment is discrete, and the voltage distribution of the storage batteries with the same capacity at the same moment is relatively concentrated; the capacity of the battery can be estimated from the voltage values according to the law.
The system comprises a storage battery on-line monitoring system, a monitoring server and a comprehensive monitoring platform, wherein the storage battery on-line monitoring system comprises back-end equipment, a sensor module, a data aggregation module, a wireless transmission module and a monitoring server/comprehensive monitoring platform; the sensor module is used for detecting parameters such as battery voltage, current and the like and sending out data through an internal bus; the data convergence module is used for realizing the signal conversion function of the communication port of the sensor module and the back-end equipment RS485 or TCP/IP; the wireless transmission module is used for transmitting data through wireless communication by using a wireless Internet of things card; the monitoring server/comprehensive monitoring platform integrates a storage battery capacity rapid estimation method, automatically calculates the current state according to the acquired storage battery voltage parameters, and performs visual processing on the data.
According to fig. 7, the on-line monitoring system for the storage battery is implemented as follows:
Step 1: according to the method shown in fig. 7, an on-line monitoring system of the storage battery is deployed on site, and the system consists of a sensor module, a data aggregation module, a wireless transmission module, a monitoring server/comprehensive monitoring platform and the like. Each storage battery is provided with a sensor module for collecting the operation data of the storage battery;
Step 2: performing capacity assessment test on the storage battery pack;
step 3: the storage battery on-line monitoring system judges that the storage battery starts to discharge the test according to the voltage and current changes of the storage battery pack or the externally given communication signal, and automatically enters a storage battery state monitoring program;
step 4: the voltage parameters of the storage battery are acquired through the sensor module, the data convergence module processes the protocol and then the protocol is transmitted into the monitoring server/the comprehensive monitoring platform through wireless;
Step 5: the monitoring server/comprehensive monitoring platform calculates the collected parameters according to the rapid estimation method of the storage battery capacity, and performs visualization processing on the data, wherein the visualization of the rapid estimation model data of the storage battery capacity is shown in fig. 8-11.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (9)
1. The method for rapidly estimating the capacity of the storage battery is characterized by comprising three core algorithms: an algorithm for obtaining a voltage standard value through big data analysis, an algorithm for judging whether the capacity of the storage battery meets the standard or not and an algorithm for predicting the capacity of the storage battery;
The algorithm for obtaining the voltage standard value through big data analysis comprises the following steps:
By collecting the storage battery capacity check data, the voltage value of the storage battery with the same capacity at a certain moment is taken as a set, the set accords with normal distribution characteristics, the expected value mu is the average value of samples, the position of the sample is determined, the standard deviation sigma determines the distribution amplitude of the sample, and the relation between the standard deviation sigma and the sample x i is shown in the expression 1: According to the probability distribution relation of a normal curve, the area in a horizontal axis interval (mu-1.96 sigma, mu+1.96 sigma) is 95.449974%, and a voltage standard value U z = mu-1.96 sigma of the capacity C at a time t 2 is obtained;
The algorithm for judging whether the capacity of the storage battery meets the standard comprises the following steps: the discharge curves of the storage batteries with different capacities show correlation between the capacities, the voltages and the voltage change rates, the rated capacities are the same, the actual capacities of the storage batteries are different, the voltage of the discharge curves is lower as the capacity is smaller, and the voltage drops faster; according to the correlation, firstly, a voltage standard value U z is obtained, and then according to the voltage standard value, the corresponding relation between the voltage and the time which can be met if the capacity of any storage battery needs to be achieved can be obtained;
The voltage standard value corresponding to the given capacity C at the time t 2 is U z, the voltage value measured at the time t 1 is U, the battery voltage measured at any time t x of t 1～t2 is U x, and the condition for judging that the battery reaches the capacity C is as follows: u x must be greater than U xz, and its correspondence is shown in expression 2:
the algorithm for predicting the capacity of the storage battery comprises the following steps: obtaining a voltage standard value U zi corresponding to the capacity C i of a certain type of storage battery at a certain time t i by utilizing big data analysis, wherein i is an integer larger than 0;
Secondly, according to an algorithm for judging whether the capacity of the storage battery reaches the standard, measuring a voltage U at a time t 1 of capacity check, obtaining voltage thresholds U x1、Ux2 corresponding to the capacities of C 1、C2 respectively according to an expression 2, and judging that the capacity of the storage battery is C 2 when the voltage U x measured at the time t x meets an expression U x2＜Ux＜Ux1;
the method for realizing the rapid estimation of the capacity of the storage battery according to the three core algorithms comprises the following steps:
Step one, obtaining voltage standard values corresponding to different capacities through big data analysis, wherein the standard values are used as key parameters for judging whether the capacity of the storage battery meets the standard or not;
step two, after obtaining the voltage standard value, according to the algorithm for judging whether the capacity of the storage battery meets the standard, we can calculate the voltage distribution rule meeting the current capacity, so that the voltage sampling value at any moment can be compared with the rule to judge whether the capacity of the storage battery meets the standard;
And thirdly, sorting the voltage standard values of the storage battery according to the corresponding capacities, sequentially comparing the voltage sampling values at any time with the sorted judgment standard, if the voltage sampling values are judged to be up to standard, the capacity of the storage battery is the capacity corresponding to the judgment standard, and if the voltage sampling values are judged to be not up to standard, selecting the judgment standard with smaller capacity for comparison, so that the capacity of the storage battery is obtained.
2. The method for rapidly estimating capacity of a storage battery according to claim 1, wherein: in the algorithm for obtaining the voltage standard value through big data analysis, the storage battery pack comprises storage batteries with the same rated capacity, the same rated voltage, the same operation duration and different performances.
3. The method for rapidly estimating capacity of a storage battery according to claim 2, wherein: the voltage hash chart after the storage battery capacity is checked for 3 hours has the following rules: the voltage distribution of the storage batteries with different capacities is discrete at the same moment, and the voltage distribution of the storage batteries with the same capacities at the same moment is relatively concentrated.
4. A method for rapid estimation of battery capacity according to claim 3, wherein: and estimating the capacity of the storage battery through the voltage value according to the rule.
5. The method for rapidly estimating capacity of a storage battery according to claim 1, wherein: the system comprises a storage battery on-line monitoring system, wherein the storage battery on-line monitoring system comprises a back-end device, a sensor module, a data aggregation module, a wireless transmission module and a monitoring server/comprehensive monitoring platform.
6. The method for rapidly estimating capacity of a storage battery according to claim 5, wherein: the sensor module is used for detecting battery voltage and current parameters and sending data through an internal bus.
7. The method for rapidly estimating capacity of a storage battery according to claim 5, wherein: the data convergence module is used for realizing the signal conversion function of the communication port of the sensor module and the back-end equipment RS485 or TCP/IP.
8. The method for rapidly estimating capacity of a storage battery according to claim 5, wherein: the wireless transmission module is used for transmitting data through wireless communication by using a wireless Internet of things card.
9. The method for rapidly estimating capacity of a storage battery according to claim 5, wherein: the monitoring server/comprehensive monitoring platform integrates a storage battery capacity rapid estimation method, automatically calculates the current state according to the acquired storage battery voltage parameters, and performs visual processing on the data.
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| CN107271905B (en) * | 2017-05-25 | 2019-12-27 | 上海思致汽车工程技术有限公司 | Battery capacity active estimation method for pure electric vehicle |
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