CN112731184B - Battery service life detection method and system - Google Patents
Battery service life detection method and system Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention provides a method and a system for detecting the service life of a battery, wherein the method comprises the following steps: acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a nominal voltage value of a battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process; calculating to obtain an actual capacity value of the battery to be detected according to the temperature and humidity value of the detection environment, the nominal capacity value and the nominal voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value; and judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, and otherwise, determining that the battery to be detected can be continuously used. The method improves the accuracy of the battery life detection.
Description
Technical Field
The invention relates to the technical field of battery detection, in particular to a method and a system for detecting the service life of a battery.
Background
At present, due to the work requirement, a large number of charging products such as flashlights, portable lamps and head lamps for emergency repair, lithium battery electric tools, interphones and the like are arranged in an electric power system. The maintenance of these rechargeable batteries and the battery life directly affect the development of the first line of electric work. In the prior art, a method for detecting the service life of a battery is a constant current discharge method, the test principle is that a constant discharge current is applied to the battery, the discharge time is accumulated, when the battery discharges until the voltage of the battery is lower than a certain voltage, the discharge is stopped, and the accumulated discharge time multiplied by the current is used for determining the capacity of the battery. Since the battery discharge process is influenced by other factors, the method for calculating the actual capacity by multiplying the discharge time by the current is inaccurate, thereby reducing the accuracy of battery life detection.
Disclosure of Invention
In order to solve the above technical problem, an aspect of the present invention provides a method for detecting a battery life, including the following steps:
acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a nominal voltage value of the battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of service life detection of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process of service life detection;
calculating to obtain an actual capacity value of the battery to be detected according to the temperature and humidity value of the detection environment, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value;
and judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, and otherwise, determining that the battery to be detected can be continuously used.
In a specific embodiment, the obtaining of the actual capacity value of the battery to be detected by calculating according to the temperature and humidity value of the detection environment, the nominal capacity value and the nominal voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger, and the discharging load resistance value specifically includes:
and inputting the temperature and humidity value of the detection environment, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value into a trained neural network model for calculation to obtain the actual capacity value of the battery to be detected.
In a specific embodiment, the establishing the neural network model specifically includes:
acquiring a plurality of groups of training data of a battery with the same type as the battery to be detected, wherein each group of training data comprises a corresponding environment temperature and humidity value, a nominal capacity value and a rated voltage value of the battery, a charging voltage value and a charging current value of a charger and a resistance value of a discharging load;
and carrying out self-adaptive learning on the multiple groups of training data to obtain a calculation function of the battery capacity.
In one embodiment, the calculation function of the battery capacity is specifically:
M＝x1*V+x2*l+x3*T+x4*N+x5*R+x6*P+x7*V1
wherein, M is the actual capacity value of the battery, x1 to x7 are corresponding coefficients, V is the charging voltage value of the charger, l is the charging current value of the charger, T is the ambient temperature value, N is the ambient humidity value, R is the resistance value of the discharging load, P is the nominal capacity value of the battery, and V1 is the rated voltage value of the battery.
In a specific embodiment, the set threshold is 50% of the nominal capacity value of the battery to be detected.
A second aspect of the present invention provides a battery life detection system, including:
the battery life detection device comprises an acquisition unit, a detection unit and a control unit, wherein the acquisition unit is used for acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a nominal voltage value of a battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of life detection of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process of life detection;
the actual capacity calculation unit is used for calculating and obtaining the actual capacity value of the battery to be detected according to the temperature and humidity value of the detection environment, the nominal capacity value and the nominal voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value;
and the judging unit is used for judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, and otherwise, determining that the battery to be detected can be continuously used.
In a specific embodiment, the actual capacity calculating unit is specifically configured to:
and inputting the temperature and humidity value of the detection environment, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value into a trained neural network model for calculation to obtain the actual capacity value of the battery to be detected.
In a specific embodiment, the establishing the neural network model specifically includes:
acquiring a plurality of groups of training data of a battery with the same type as the battery to be detected, wherein each group of training data comprises a corresponding environment temperature and humidity value, a nominal capacity value and a rated voltage value of the battery, a charging voltage value and a charging current value of a charger and a resistance value of a discharging load;
and carrying out self-adaptive learning on the multiple groups of training data to obtain a calculation function of the battery capacity.
In one embodiment, the calculation function of the battery capacity is specifically:
M＝x1*V+x2*l+x3*T+x4*N+x5*R+x6*P+x7*V1
wherein, M is the actual capacity value of the battery, x1 to x7 are corresponding coefficients, V is the charging voltage value of the charger, l is the charging current value of the charger, T is the ambient temperature value, N is the ambient humidity value, R is the resistance value of the discharging load, P is the nominal capacity value of the battery, and V1 is the rated voltage value of the battery.
In a specific embodiment, the set threshold is 50% of the nominal capacity of the battery to be detected.
The embodiment of the invention has the beneficial effects that: the battery life detection method comprises the steps of obtaining the ambient temperature and humidity, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value and the charging current value of a charger for charging the battery to be detected in the charging process of the battery to be detected, and the discharging load resistance value of the battery to be detected in the discharging process, obtaining the actual capacity of the battery to be detected by utilizing a machine learning algorithm according to the values, and comparing the actual capacity of the battery to be detected with a set threshold value, so as to determine whether the service life of the battery to be detected is reached. The method considers a plurality of factors influencing the actual capacity of the battery, determines a specific calculation model and improves the accuracy of judging the service life of the battery.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting battery life according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, a method for detecting a battery life according to an embodiment of the present invention includes the following steps:
s1, acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a nominal voltage value of the battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of service life detection of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process of service life detection.
Specifically, a temperature value and a humidity value of a detection environment are obtained, nominal capacity and rated voltage of a battery to be detected are obtained, the battery to be detected is placed on a detection module to be detected, automatic charging and automatic discharging are started, the battery detection module uploads detected battery information to an industrial personal computer in the form of electric signals, the industrial personal computer uploads the signals to a cloud platform through a 4G or 5G transmission module, charging voltage and charging current of a charger which charges the battery to be detected in the charging process of the battery to be detected are recorded, a resistance value of a discharging load of the battery to be detected in the discharging process of the battery to be detected is recorded, and the resistance of the discharging load is discharging load resistance in the discharging process of the battery.
And S2, calculating according to the temperature and humidity of the detection environment, the nominal capacity and the rated voltage of the battery to be detected, the charging voltage of the charger, the charging current of the charger and the discharging load resistance value to obtain the actual capacity of the battery to be detected.
Specifically, the temperature and humidity value of the detection environment, the nominal capacity and the rated voltage of the battery to be detected, the charging voltage of the charger, the charging current of the charger and the discharging load resistance value are input into a trained neural network model, and the actual capacity of the battery to be detected is obtained through calculation according to the temperature and humidity value of the detection environment, the nominal capacity and the rated voltage of the battery to be detected, the charging voltage of the charger, the charging current of the charger and the discharging load resistance value.
Specifically, the neural network model has 3 layers, namely an input layer, a hidden layer and an output layer, wherein the number of units of the input layer is 7, and the number of units of the output layer is 1.
Specifically, in order to train the neural network model, training data needs to be collected in advance, and in a specific embodiment, temperature and humidity values of a plurality of detection environments, nominal capacity values and rated voltage values of a plurality of batteries of the same type as the battery to be detected, corresponding charging voltage values of the charger, charging current values of the charger, resistance values of a discharging load, and actual capacity values of the battery are collected, so that a plurality of groups of training sets are formed. For each training set, the corresponding ambient temperature and humidity, the nominal capacity value, the nominal voltage value, the charging current value of the charger, and the resistance value of the discharging load of the battery under the ambient temperature and humidity condition are the inputs of the training set, and the actual capacity value of the battery is the output of the training set. Each set of training sets is processed such that the processed values are between-1 and 1 or-0.5-0.5, and weight values are initialized. The following procedure is performed for each training set:
and inputting the input of each training set into the neural network model, performing convolution calculation on the input of each training set and the corresponding weight coefficient to obtain prediction output, and calculating according to the output of the training set and the prediction output to obtain the prediction error of the output layer. And adjusting the weight value according to the prediction error, calculating by using the adjusted weight value, and repeating the steps until a termination condition is reached, wherein in a specific embodiment, the termination condition is that the difference between the predicted battery capacity value and the battery nominal capacity value is less than or equal to 10mAh. Recording the weight value when the training reaches the termination condition, and obtaining a calculation function of the actual capacity of the battery according to the weight value when the termination condition is met, wherein the calculation function comprises the following steps:
M＝x1*V+x2*l+x3*T+x4*N+x5*R+x6*P+x7*V1
wherein, M is the actual capacity value of the battery, x1 to x7 are corresponding coefficients, i.e. weight values at the end of training, V is the charging voltage value of the charger, l is the charging current value of the charger, T is the ambient temperature value, N is the ambient humidity value, R is the resistance value of the discharging load, P is the nominal capacity value of the battery, and V1 is the rated voltage value of the battery.
After a calculation function of the actual capacity of the battery is obtained by constructing a neural network model, substituting the temperature and humidity value of the environment obtained by the detection, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value and the charging current value of a charger for charging the battery to be detected in the charging process of the life detection of the battery to be detected, and the discharging load resistance value of the battery to be detected in the discharging process of the life detection into the calculation function, and calculating to obtain the actual capacity value of the battery to be detected.
And S3, judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, otherwise, determining that the battery to be detected can be continuously used.
Specifically, whether the actual capacity is smaller than a set threshold value or not is judged, if yes, the capacity of the battery to be detected does not reach the standard, scrapping treatment is recommended, and if not, the battery to be detected does not reach the service life, continued use is recommended.
In one embodiment, the set threshold may be 50% of the nominal capacity of the battery to be tested. Namely, when the actual capacity value of the battery to be detected is smaller than 50% of the nominal capacity, the battery to be detected reaches the service life, and scrapping is recommended, otherwise, the battery to be detected does not reach the service life, and continuous use is recommended.
According to the battery life detection method provided by the embodiment of the invention, the ambient temperature and humidity, the nominal capacity value and the nominal voltage value of the battery to be detected, the charging voltage value and the charging current value of a charger for charging the battery to be detected in the charging process of the battery to be detected, and the discharging load resistance value of the battery to be detected in the discharging process are obtained, the actual capacity of the battery to be detected is obtained through the values by using a machine learning algorithm, and the actual capacity of the battery to be detected is compared with the set threshold value, so that whether the service life of the battery to be detected is reached is determined. The method considers a plurality of factors influencing the actual capacity of the battery, determines a specific calculation model and improves the accuracy of judging the service life of the battery.
Based on the first embodiment of the present invention, the second embodiment of the present invention provides a system for detecting a battery life, which specifically includes: the device comprises an acquisition unit, an actual capacity calculation unit and a judgment unit, wherein the acquisition unit is used for acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a rated voltage value of a battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process; the actual capacity calculation unit is used for calculating and obtaining the actual capacity value of the battery to be detected according to the temperature and humidity value of the detection environment, the nominal capacity value and the nominal voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value; the judging unit is used for judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, and otherwise, determining that the battery to be detected can be continuously used.
In a specific embodiment, the actual capacity calculating unit is specifically configured to: and inputting the temperature and humidity value of the detection environment, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the resistance value of the discharging load into a trained neural network model for calculation to obtain the actual capacity value of the battery to be detected.
In a specific embodiment, the establishing the neural network model specifically includes: acquiring a plurality of groups of training data of a battery with the same type as the battery to be detected, wherein each group of training data comprises a corresponding environment temperature and humidity value, a nominal capacity value and a rated voltage value of the battery, a charging voltage value and a charging current value of a charger and a resistance value of a discharging load; and carrying out self-adaptive learning on the multiple groups of training data to obtain a calculation function of the battery capacity.
In a specific embodiment, the calculation function of the battery capacity is specifically:
M＝x1*V+x2*l+x3*T+x4*N+x5*R+x6*P+x7*V1
wherein, M is the actual capacity value of the battery, x1 to x7 are corresponding coefficients, V is the charging voltage value of the charger, l is the charging current value of the charger, T is the ambient temperature value, N is the ambient humidity value, R is the resistance value of the discharging load, P is the nominal capacity value of the battery, and V1 is the rated voltage value of the battery.
In a specific embodiment, the set threshold is 50% of the nominal capacity of the battery to be detected.
For the working principle and the advantageous effects thereof, please refer to the description of the first embodiment of the present invention, which will not be described herein again.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (6)
1. A method for detecting battery life, comprising:
acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a nominal voltage value of a battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of service life detection of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process of service life detection;
inputting the temperature and humidity value of the detection environment, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger and the discharging load resistance value into a trained neural network model for calculation to obtain the actual capacity value of the battery to be detected, wherein the calculation function of the actual capacity value of the battery to be detected is specifically as follows:
M＝x1*V+x2*l+x3*T+x4*N+x5*R+x6*P+x7*V1
wherein M is an actual capacity value of the battery, x1 to x7 are corresponding coefficients, V is a charging voltage value of the charger, l is a charging current value of the charger, T is an environmental temperature value, N is an environmental humidity value, R is a resistance value of a discharging load, P is a nominal capacity value of the battery, and V1 is a rated voltage value of the battery;
and judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, otherwise, determining that the battery to be detected can be continuously used.
2. The method according to claim 1, wherein the training process of the neural network model specifically comprises:
acquiring a plurality of groups of training data of a battery with the same type as the battery to be detected, wherein each group of training data comprises a corresponding environment temperature and humidity value, a nominal capacity value and a rated voltage value of the battery, a charging voltage value and a charging current value of a charger and a resistance value of a discharging load;
and carrying out self-adaptive learning on the multiple groups of training data to obtain a calculation function of the battery capacity.
3. The detection method according to claim 2, characterized in that the set threshold value is 50% of the nominal capacity value of the battery to be detected.
4. A battery life detection system, comprising:
the battery life detection device comprises an acquisition unit, a detection unit and a control unit, wherein the acquisition unit is used for acquiring a temperature and humidity value of a detection environment, a nominal capacity value and a rated voltage value of a battery to be detected, a charging voltage value and a charging current value of a charger for charging the battery to be detected in a charging process of life detection of the battery to be detected, and a discharging load resistance value of the battery to be detected in a discharging process of life detection;
the actual capacity calculation unit is configured to input the temperature and humidity value of the detection environment, the nominal capacity value and the rated voltage value of the battery to be detected, the charging voltage value of the charger, the charging current value of the charger, and the discharging load resistance value into a trained neural network model to calculate and obtain the actual capacity value of the battery to be detected, where a calculation function of the actual capacity value of the battery to be detected specifically is as follows:
M＝x1*V+x2*l+x3*T+x4*N+x5*R+x6*P+x7*V1
wherein M is an actual capacity value of the battery, x1 to x7 are corresponding coefficients, V is a charging voltage value of the charger, l is a charging current value of the charger, T is an environmental temperature value, N is an environmental humidity value, R is a resistance value of a discharging load, P is a nominal capacity value of the battery, and V1 is a rated voltage value of the battery;
and the judging unit is used for judging whether the actual capacity value is smaller than a set threshold value, if so, determining that the service life of the battery to be detected is reached, and otherwise, determining that the battery to be detected can be continuously used.
5. The detection system according to claim 4, wherein the training process of the neural network model specifically includes:
acquiring a plurality of groups of training data of a battery with the same type as the battery to be detected, wherein each group of training data comprises a corresponding environment temperature and humidity value, a nominal capacity value and a rated voltage value of the battery, a charging voltage value and a charging current value of a charger and a resistance value of a discharging load;
and carrying out self-adaptive learning on the multiple groups of training data to obtain a calculation function of the battery capacity.
6. The detection system according to claim 5, wherein the set threshold is 50% of the nominal capacity of the battery to be detected.
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| CN114047452B (en) * | 2022-01-13 | 2022-05-13 | 浙江玥视科技有限公司 | Method and device for determining cycle life of battery |
| CN116600228B (en) * | 2023-07-18 | 2024-03-29 | 深圳市鑫正宇科技有限公司 | Communication system and method for wireless earphone and earphone storage box |
| CN116930790B (en) * | 2023-09-15 | 2023-11-21 | 深圳市海雷新能源有限公司 | Electric quantity calibration method for intelligent battery |
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