CN113740739B - Vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method - Google Patents
Vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method Download PDFInfo
- Publication number
- CN113740739B CN113740739B CN202110831688.8A CN202110831688A CN113740739B CN 113740739 B CN113740739 B CN 113740739B CN 202110831688 A CN202110831688 A CN 202110831688A CN 113740739 B CN113740739 B CN 113740739B
- Authority
- CN
- China
- Prior art keywords
- battery
- soh
- storage battery
- discharge
- voltage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 239000002253 acid Substances 0.000 title claims abstract description 30
- 230000008859 change Effects 0.000 claims abstract description 40
- 238000012360 testing method Methods 0.000 claims abstract description 22
- 238000013528 artificial neural network Methods 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000007599 discharging Methods 0.000 claims abstract description 14
- 230000036541 health Effects 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 14
- 238000007600 charging Methods 0.000 claims description 8
- 238000010277 constant-current charging Methods 0.000 claims description 4
- 238000002474 experimental method Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 125000004122 cyclic group Chemical group 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 6
- 239000002699 waste material Substances 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 abstract description 3
- 230000001154 acute effect Effects 0.000 abstract 1
- 238000011156 evaluation Methods 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 3
- 239000003990 capacitor Substances 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000007667 floating Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
Images
Classifications
-
- 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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
- G01R31/379—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator for lead-acid batteries
-
- 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- 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
- H01M10/4285—Testing apparatus
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- 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
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/482—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for several batteries or cells simultaneously or sequentially
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Manufacturing & Machinery (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- General Chemical & Material Sciences (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses a vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method, which is based on the principle that the sensing of the change rate of battery terminal voltage to SOH change in the constant current discharging process is more acute than the SOH change per se, and the storage battery is subjected to multi-parameter measurement by defining an SOH estimation mode, wherein the multi-parameter measurement comprises the terminal voltage, ohmic internal resistance, the change rate of the terminal voltage to time and the change rate of the terminal voltage to SOC in the working process of the storage battery, the parameters are subjected to fitting by a multi-layer neural network to obtain a trained multi-layer neural network, then a discharge test is carried out on a battery to be tested, the data is obtained, the SOH of the battery is predicted, the residual life is obtained according to an SOH-cycle number curve, so that the battery can be maintained and replaced in time, and the reliability of equipment is ensured. The invention can ensure the continuous application of the battery with perfect performance to the greatest extent by improving the detection accuracy and efficiency, reduce the purchase cost of the battery, reduce the waste and save the cost.
Description
Technical Field
The invention belongs to the field of battery management, and particularly relates to a method for predicting the residual life of a vehicle-mounted starting maintenance-free lead-acid storage battery.
Background
The lead-acid battery has the advantages of universality, low cost, high efficiency, moderate specific energy, long service life and the like, and is widely applied to various fields of production and life. For example, most uninterruptible power supplies in the dc system of the substation in China use VRLA batteries. The battery is in a floating charge state under normal conditions, and when the power grid is accidentally lost, the battery can provide emergency electric energy supply for loads such as a control loop, a signal loop, a relay protection automatic device, a breaker switching operation mechanism and the like. In addition, lead acid batteries are also commonly used as a starting and auxiliary power source for vehicles. Batteries in vehicles have a variety of functions: when the engine is started, the battery provides strong starting current for the engine; when the engine is at idle speed or the automobile generator is overloaded, the battery supplies power to electric equipment in the automobile; when the generator terminal voltage is higher than the battery electromotive force, the battery is in a charged state. Therefore, the lead-acid battery in the vehicle has a plurality of working modes such as high-rate pulse discharge, rated-rate charge and discharge, floating charge operation and the like.
From the demand of lead acid batteries, a decrease in the capacity of the battery to less than 80% is generally considered an unacceptable level of battery, and thus the remaining life is determined by a decrease in battery state of health of less than 0.8.
In the research, the health state research of the lead-acid battery is closely related to the prediction of the residual life, the premise of determining the residual life is to obtain the current health state of the battery, and based on the health state, the residual life is estimated and predicted by utilizing various models.
The service life of a storage battery in weapon equipment such as a vehicle is related to various factors such as vehicle conditions, road conditions, habits of drivers, power levels of internal equipment, working modes and the like. The service life of lead-acid batteries in civilian vehicles is typically around 3 years, while lead-acid batteries in military vehicles are typically shorter due to more complex and harsh use environments. If periodic maintenance and status evaluations and life predictions are not performed on lead-acid batteries in vehicles, it is possible that the normal use of the entire vehicle equipment may be affected by sudden failure of the battery. Therefore, it is necessary to perform theoretical and experimental researches on a state evaluation method and a residual life prediction of a lead-acid battery in a vehicle and other weaponry, design a state evaluation method for a vehicle and other weaponry lead-acid battery, and develop a corresponding detection device, so as to ensure the reliability of the vehicle and other weaponry.
In the prior art, single influencing factors such as open circuit voltage and ohmic internal resistance of the storage battery are generally adopted to evaluate the health condition of the storage battery, but the SOH of the storage battery is often not only related to one influencing factor, and single factor measurement may not be accurate and has larger error, so that the technologies cannot accurately predict the SOH of the storage battery, and therefore, a plurality of influencing factors are required to be combined to comprehensively evaluate the health condition of the storage battery.
Disclosure of Invention
The invention aims to provide a vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method. The invention mainly builds state evaluation based on the external characteristic parameters of the battery, designs a residual life prediction model and algorithm and develops application.
The invention is realized by adopting the following technical scheme:
a vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method comprises the following steps:
1) Carrying out charge-discharge cycle test on a certain type of battery in advance to obtain a plurality of battery data including battery terminal voltage, terminal voltage change rate along with time, terminal voltage change rate along with SOC and ohmic internal resistance of an equivalent circuit of the battery in each cycle, and carrying out nuclear capacity test on the battery at intervals of fixed times to determine SOH of the battery;
2) Training the multi-layer neural network by using the battery data obtained by the charge-discharge cycle tests, finally obtaining a trained multi-layer neural network, and fitting an SOH-cycle number curve according to the measured SOH;
3) And (3) carrying out discharge test on the battery to be tested of the same model to obtain data of terminal voltage of the storage battery, the change rate of the terminal voltage along with time, the change rate of the terminal voltage along with the SOC and the ohmic internal resistance of an equivalent circuit of the storage battery, importing the data into a trained multi-layer neural network to obtain a predicted SOH value, defining a health grade, and obtaining the residual cycle times of the battery, namely the residual life by using a pre-fitted SOH-cycle time curve.
The invention is further improved in that the specific implementation method of the step 1) is as follows:
101 Considering that the lead-acid battery cannot be fully charged and discharged in practical application, and discharging and charging are carried out at a certain depth of discharge, the following steps are used for carrying out charge-discharge cycle tests on the storage battery and recording data:
(1) the accumulator is controlled at 25+/-10 ℃ and 2I 20 (A) Constant current, when the end voltage of the storage battery reaches 14.80V, I is adopted 20 (A) Constant current charging for 4 hours;
(2) after the storage battery is fully charged, the ambient temperature of 25+/-2 ℃ is kept at 5I n Discharge for 1h, I n A discharge current of 20 h;
(3) charging with voltage of 14.40V + -0.10V for 2h55min, maximum current limit of 10I n ；
(4) Then with charging current 0.5I n Charging for 5min;
(5) standing for 30min;
(6) the (2) - (5) form a complete test cycle, the terminal voltage of the storage battery is not lower than 10.50V when the storage battery is circularly discharged, otherwise, the test is terminated;
102 Every 20 cycles, performing a nuclear capacity experiment on the storage battery for measuring the SOH of the battery, and fitting out an SOH-cycle number curve by using a least square method, wherein the nuclear capacity experiment comprises the following steps:
(1) after the storage battery is fully charged, the temperature is within 1-5 hours under the environmental condition of 25+/-2 ℃ and I is as follows 20 (A) The current discharge, the change of the current value should not exceed +/-2% in the discharge process, the end voltage of the storage battery is recorded every 2 hours in the discharge process, and the temperature of the battery is recorded every 4 hours;
(2) when the voltage reaches 10.80V, recording the voltage once every 5min, and when the voltage reaches 10.50V plus or minus 0.05V, stopping discharging and recording the discharging time and the discharging temperature;
103 The historical data of the battery is obtained, including the terminal voltage of the battery, the change rate of the terminal voltage along with time, the change rate of the terminal voltage along with the SOC and the ohmic internal resistance of an equivalent circuit of the battery, and the historical data of the battery is used for training a multi-layer neural network.
The invention is further improved in that the specific implementation method of the step 2) is as follows:
201 Dividing the plurality of battery data obtained in step 1) into two parts: one part is a training data set, and the other part is a verification data set;
202 A small batch algorithm is used for processing the training data set, namely, a mixed form of the SGD algorithm and the batch algorithm is adopted, a part of training data set is selected firstly, the batch algorithm is used for training the data set, a ReLU function and a node discarding algorithm are used, then an average weight updating value is used for adjusting the neural network, and the process is repeated until the battery history data are used;
203 Using the verification set to evaluate the performance of the model, and ending training if the preset performance is obtained; if not, modifying the model, and repeating the first step.
The invention is further improved in that the specific implementation method of the step 3) is as follows:
301 The method comprises the following steps of:
(1) the accumulator is controlled at 25+/-10 ℃ and 2I 20 (A) Constant current, when the end voltage of the storage battery reaches 14.80V, I is adopted 20 (A) Constant current charging for 4 hours;
(2) after the storage battery is fully charged, the ambient temperature of 25+/-2 ℃ is kept at 5I n Discharging for 1h;
302 Importing the data into a trained multi-layer neural network to obtain a predicted SOH value;
303 The predicted SOH value is led into a pre-fitted SOH-cycle number curve to obtain the residual cycle number of the battery from the current SOH to soh=80%, namely the residual life.
The invention is further improved in that in step 3), the battery is divided into four health classes:
1) S level: the storage battery is very good in health state, and SOH is more than or equal to 95%;
2) Class a: the health state of the storage battery is good, SOH is more than or equal to 85% and less than 95%;
3) B level: the storage battery can still be used continuously, but the residual life is less, SOH is more than or equal to 80% and less than 85%;
4) C level: the storage battery needs to be replaced, and SOH is less than 80%.
The invention is further improved in that in the step 1), a large number of cyclic charge and discharge tests show that in the constant current discharge process, a linear relationship exists between the SOC and the terminal voltage of the storage battery, namely SOC=aU 0 +b。
A further development of the invention is that in step 1), the terminal voltage isU 0 There is a relationship between the derivative of time t and SOH, which can be used as a parameter for predicting SOH.
A further development of the invention is that in step 1), a linear relationship exists between the SOC and the terminal voltage of the battery during constant current discharge, i.e. soc=au 0 +b, the slope of the function increases according to the decrease of SOH, i.e. the smaller SOH, the greater the rate of change of voltage to SOC, so the terminal voltage U 0 There is also a relationship between the derivative of SOC and SOH, and this can be used as a parameter for predicting SOH.
The invention is further improved in that in the step 1), the method for estimating SOH based on the voltage change rate of the battery during constant current discharge comprises the following steps:
wherein K is end Represents the rate of change, K, of the voltage at which the battery decays to the end of life at a certain depth of discharge now Representing the rate of change, K, of the voltage in the current cycle of the battery at this depth of discharge 0 Indicating the rate of change of voltage from the first discharge of the new battery to the depth of discharge.
The invention further improves the method for predicting the SOH of the storage battery by measuring various circuit parameters of the storage battery in the constant-current discharging process and fitting test data through a multi-layer neural network.
Compared with the prior art, the invention has at least the following beneficial technical effects:
the invention provides a vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method, which is different from the existing single factor prediction scheme, and adopts multiple influencing factors to predict the SOH of a storage battery, wherein the relation between each influencing factor and the SOH of the storage battery is as follows:
in the constant current discharge process, the relation between terminal voltage and storage battery SOH:
relationship between the rate of change of terminal voltage versus time and battery SOH:
wherein,,
C b the energy storage capacitor is an indicator capacitor of Randes equivalent circuit, namely SOH and SOC;
relationship between the rate of change of terminal voltage to SOC and battery SOH: there is a certain linear relationship between SOC and terminal voltage of the battery, i.e. soc=au 0 +b, but it was found by a large number of charge-discharge cycle tests that the slope of the function was not constant but increased according to the decrease of SOH, i.e., the smaller SOH, the greater the rate of change of voltage to SOC, so the terminal voltage U 0 There is also a relationship between the derivative of SOC and SOH.
The method for estimating SOH based on the voltage change rate of the battery in constant current discharge is provided:
wherein K is end Represents the rate of change, K, of the voltage at which the battery decays to the end of life at a certain depth of discharge now Representing the rate of change, K, of the voltage in the current cycle of the battery at this depth of discharge 0 Indicating the rate of change of voltage from the first discharge of the new battery to the depth of discharge.
And inputting the influence factor data into a trained neural network to obtain the predicted SOH of the storage battery.
The invention builds a state evaluation and residual life prediction model and algorithm based on external characteristic parameters of a battery, and provides a method capable of realizing the prediction of the residual life of the battery. Meanwhile, by improving the detection accuracy and efficiency, the battery with perfect performance can be ensured to be continuously applied to the greatest extent, the purchase cost of the battery is reduced, the waste is reduced, and the cost is saved.
The lead-acid battery residual life prediction method can be widely applied to various weaponry, including self-propelled artillery, tanks, armored vehicles, working battery packs of vehicles, battery packs provided with power supply, battery packs provided with diesel-electric submarines, standby battery packs and the like. According to the invention, different working modes of lead-acid batteries in different equipment are researched, and residual life prediction models of different batteries are established, so that the state and the expected residual life of the batteries are obtained, the batteries are maintained and replaced in time, and the reliability of the equipment is ensured. Meanwhile, by improving the detection accuracy and efficiency, the battery with perfect performance can be ensured to be continuously applied to the greatest extent, the purchase cost of the battery is reduced, the waste is reduced, and the cost is saved.
The completion of the invention greatly improves the detection and maintenance efficiency of the lead-acid battery in army weaponry, ensures that the equipment is in the optimal working state at any time, meets the requirements of army cross-region motor combat equipment guarantee and the maintenance guarantee requirement of novel equipment, drives and improves the maintenance capability of basic army, improves the combat force of the army and powerfully ensures the completion of tasks.
Drawings
FIG. 1 is a flow chart of a method for predicting the remaining life of a vehicle-mounted start-up maintenance-free lead-acid battery;
FIG. 2 is a diagram of a lead-acid battery randes equivalent circuit;
FIG. 3 is a graph showing the linear relationship between terminal voltage and SOC during constant current discharge of the battery;
FIG. 4 is a flow chart for battery SOH estimation;
FIG. 5 is a flow chart of a study of the experimental data processing section;
fig. 6 is a fitted SOH-cycle number image.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in fig. 1, the method for predicting the residual life of the vehicle-mounted starting maintenance-free lead-acid storage battery provided by the invention measures the terminal voltage and the terminal voltage in the working process of the storage battery, the change rate of the SOC and the time and the ohmic internal resistance, and introduces a pre-trained multi-layer neural network to estimate the SOH of the storage battery in the state, so that the purpose of predicting the current state of the battery is achieved. The invention can maintain and replace the battery in time, and ensure the reliability of equipment. Meanwhile, by improving the detection accuracy and efficiency, the battery with perfect performance can be ensured to be continuously applied to the greatest extent, the purchase cost of the battery is reduced, the waste is reduced, and the cost is saved.
Specifically, by SOC and U 0 The linear relationship between SOH and SOC demonstrates that terminal voltage can be used as a parameter for predicting SOH. Current actual maximum capacity Q of accumulator now And energy storage capacitance parameter C in lead-acid storage battery Randes equivalent circuit b There is a certain linear relationship, i.e. Q now ＝pC b +q, where p, q are constants and SOH is defined as the ratio of the current actual maximum capacity of the battery to the rated capacity of the new battery, i.e
The following formula can be carried: />
SOH and parameter C b There is also a linear relationship between them as shown in fig. 6. And the following relationship exists between SOC and SOH:
then, it is possible to obtain:
u can be known 0 There is a relationship with SOH. Therefore, through a large number of charge-discharge cycle tests on the storage battery, corresponding parameters can be obtained so as to predict the SOH of the lead-acid storage battery.
In particular, since the rate of change of battery terminal voltage is more sensitive to SOH changes than terminal voltage itself does, multiple impact factors are used to predict SOH of the battery.
Specifically, as shown in FIG. 2, a Randes equivalent circuit model of the lead-acid storage battery is established, and the terminal voltage U is verified 0 There is also a certain relation between the derivative of time t and SOH, which can be used as a parameter for predicting SOH.
The following equation exists in the circuit:
U 0 ＝U cb +U cs +IR i
in the constant current discharge process, I is a constant, so U 0 The derivative of t can be obtained:
and SOH and parameter C b There is a linear relation between
So terminal voltage U 0 There is a certain relation between the derivative of time t and SOH, which can be used as a parameter for predicting SOH.
Specifically, as shown in FIG. 3, the SOC and the terminal voltage U are verified by a large number of lead-acid storage battery charge-discharge cycle tests 0 There is a linear relationship between them, but under different SOH conditions, the slope of the voltage versus the rate of change of SOC is not constant, but rather is dependent onThe decrease and increase of SOH, that is, the smaller SOH, the greater the rate of change of voltage to SOC, so the terminal voltage U 0 There is also a certain relationship between the derivative of SOC and SOH, which can be used as a parameter for predicting SOH.
Specifically, a new SOH estimation method is defined:
specifically, as shown in fig. 4, the current SOH of the battery is predicted by collecting various parameters during the operation of the battery and using the fitting result of the multi-layer neural network.
Specifically, as shown in fig. 5, a study flow of the experimental data processing section is given.
The above is merely to illustrate the technical idea of the present invention, but the present invention is not limited to the above-described embodiments. Any modification made on the basis of the technical scheme according to the technical idea provided by the invention without departing from the principle and spirit of the invention falls within the protection scope of the claims of the invention.
Claims (2)
1. The method is characterized in that the method is used for measuring various circuit parameters of the storage battery in the constant-current discharging process, and the SOH of the storage battery is predicted by fitting test data through a multi-layer neural network, and specifically comprises the following steps:
1) Carrying out charge-discharge cycle test on a certain type of battery in advance to obtain a plurality of battery data including battery terminal voltage, terminal voltage change rate along with time, terminal voltage change rate along with SOC and ohmic internal resistance of an equivalent circuit of the battery in each cycle, and carrying out nuclear capacity test on the battery at intervals of fixed times to determine SOH of the battery; the specific implementation method is as follows:
101 Considering that the lead-acid battery cannot be fully charged and discharged in practical application, and discharging and charging are carried out at a certain depth of discharge, the following steps are used for carrying out charge-discharge cycle tests on the storage battery and recording data:
(1) the accumulator is controlled at 25+/-10 ℃ and 2I 20 (A) Constant current, when the end voltage of the storage battery reaches 14.80V, I is adopted 20 (A) Constant current charging for 4 hours;
(2) after the storage battery is fully charged, the ambient temperature of 25+/-2 ℃ is kept at 5I n Discharge for 1h, I n A discharge current of 20 h;
(3) charging with voltage of 14.40V + -0.10V for 2h55min, maximum current limit of 10I n ；
(4) Then with charging current 0.5I n Charging for 5min;
(5) standing for 30min;
(6) the (2) - (5) form a complete test cycle, the terminal voltage of the storage battery is not lower than 10.50V when the storage battery is circularly discharged, otherwise, the test is terminated;
102 Every 20 cycles, performing a nuclear capacity experiment on the storage battery for measuring the SOH of the battery, and fitting out an SOH-cycle number curve by using a least square method, wherein the nuclear capacity experiment comprises the following steps:
(1) after the storage battery is fully charged, the temperature is within 1-5 hours under the environmental condition of 25+/-2 ℃ and I is as follows 20 (A) The current discharge, the change of the current value should not exceed +/-2% in the discharge process, the end voltage of the storage battery is recorded every 2 hours in the discharge process, and the temperature of the battery is recorded every 4 hours;
(2) when the voltage reaches 10.80V, recording the voltage once every 5min, and when the voltage reaches 10.50V plus or minus 0.05V, stopping discharging and recording the discharging time and the discharging temperature;
103 Obtaining battery historical data comprising end voltage of the storage battery, change rate of the end voltage along with time, change rate of the end voltage along with SOC and ohmic internal resistance of an equivalent circuit of the storage battery, and training a multi-layer neural network;
the method for estimating SOH based on the voltage change rate of the battery during constant-current discharge comprises the following steps:
wherein K is end Is shown at a certain positionRate of change, K, of battery decay to voltage at end of life at depth of discharge now Representing the rate of change, K, of the voltage in the current cycle of the battery at this depth of discharge 0 Representing the rate of change of the voltage of the new battery from the first discharge to the depth of discharge;
through a large number of cyclic charge and discharge tests, a linear relationship exists between the SOC and the terminal voltage of the storage battery in the constant current discharge process, namely SOC=aU 0 +b；
Terminal voltage U 0 The derivative of time t has a relation with SOH and can be used as a parameter for predicting SOH;
in the constant-current discharging process, a linear relationship exists between the SOC of the storage battery and the terminal voltage, namely SOC=aU 0 +b, the slope of the function increases according to the decrease of SOH, i.e. the smaller SOH, the greater the rate of change of voltage to SOC, so the terminal voltage U 0 There is also a relationship between the derivative of SOC and SOH, which can be used as a parameter for predicting SOH;
2) Training the multi-layer neural network by using the battery data obtained by the charge-discharge cycle tests, finally obtaining a trained multi-layer neural network, and fitting an SOH-cycle number curve according to the measured SOH; the specific implementation method is as follows:
201 Dividing the plurality of battery data obtained in step 1) into two parts: one part is a training data set, and the other part is a verification data set;
202 A small batch algorithm is used for processing the training data set, namely, a mixed form of the SGD algorithm and the batch algorithm is adopted, a part of training data set is selected firstly, the batch algorithm is used for training the data set, a ReLU function and a node discarding algorithm are used, then an average weight updating value is used for adjusting the neural network, and the process is repeated until the battery history data are used;
203 Using the verification set to evaluate the performance of the model, and ending training if the preset performance is obtained; if not, modifying the model, and repeating the first step;
3) Performing discharge test on the battery to be tested of the same model to obtain data such as battery terminal voltage, terminal voltage change rate along with time, terminal voltage change rate along with SOC and ohmic internal resistance of a battery equivalent circuit, importing the data into a trained multi-layer neural network to obtain a predicted SOH value, defining a health grade, and obtaining the residual cycle times of the battery, namely the residual life by using a pre-fitted SOH-cycle time curve; the specific implementation method is as follows:
301 The method comprises the following steps of:
(1) the accumulator is controlled at 25+/-10 ℃ and 2I 20 (A) Constant current, when the end voltage of the storage battery reaches 14.80V, I is adopted 20 (A) Constant current charging for 4 hours;
(2) after the storage battery is fully charged, the ambient temperature of 25+/-2 ℃ is kept at 5I n Discharging for 1h;
302 Importing the data into a trained multi-layer neural network to obtain a predicted SOH value;
303 The predicted SOH value is led into a pre-fitted SOH-cycle number curve to obtain the residual cycle number of the battery from the current SOH to soh=80%, namely the residual life.
2. The method for predicting the remaining life of a vehicle-mounted start-up maintenance-free lead-acid battery according to claim 1, wherein in step 3), the battery is classified into four health levels:
1) S level: the storage battery is very good in health state, and SOH is more than or equal to 95%;
2) Class a: the health state of the storage battery is good, SOH is more than or equal to 85% and less than 95%;
3) B level: the storage battery can still be used continuously, but the residual life is less, SOH is more than or equal to 80% and less than 85%;
4) C level: the storage battery needs to be replaced, and SOH is less than 80%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110831688.8A CN113740739B (en) | 2021-07-22 | 2021-07-22 | Vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110831688.8A CN113740739B (en) | 2021-07-22 | 2021-07-22 | Vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113740739A CN113740739A (en) | 2021-12-03 |
| CN113740739B true CN113740739B (en) | 2023-05-02 |
Family
ID=78728988
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110831688.8A Active CN113740739B (en) | 2021-07-22 | 2021-07-22 | Vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113740739B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115616415B (en) * | 2022-12-06 | 2023-04-07 | 北京志翔科技股份有限公司 | Method, device and equipment for evaluating state of battery pack and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110850298A (en) * | 2019-10-29 | 2020-02-28 | 上海交通大学 | Lithium battery SOH estimation method and system based on data driving |
| CN112269134A (en) * | 2020-09-10 | 2021-01-26 | 杭州电子科技大学 | Battery SOC and SOH joint estimation method based on deep learning |
| CN113093014A (en) * | 2021-03-31 | 2021-07-09 | 山东建筑大学 | Online collaborative estimation method and system for SOH and SOC based on impedance parameters |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106353687B (en) * | 2016-08-26 | 2020-12-04 | 中国电力科学研究院 | Lithium battery health state assessment method |
| US10209314B2 (en) * | 2016-11-21 | 2019-02-19 | Battelle Energy Alliance, Llc | Systems and methods for estimation and prediction of battery health and performance |
| US11637331B2 (en) * | 2017-11-20 | 2023-04-25 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge and state of health estimation |
| CN109738810B (en) * | 2019-01-21 | 2021-06-15 | 温州大学 | Comprehensive estimation method for remaining service life of battery |
| KR102387780B1 (en) * | 2020-03-30 | 2022-04-18 | 주식회사 아르고스다인 | Method and apparatus for estimating battery capacity based on neural network |
| CN111983459B (en) * | 2020-07-18 | 2023-06-06 | 武汉数值仿真技术研究院有限公司 | Health state test evaluation method based on vehicle lithium ion battery |
| CN112540318B (en) * | 2020-12-22 | 2022-01-04 | 武汉理工大学 | Method for estimating health state of lead-acid storage battery for starting internal combustion engine |
| CN112580211A (en) * | 2020-12-23 | 2021-03-30 | 天津大学 | Lead-acid storage battery SOH estimation method based on SA and ANN algorithm |
| CN112946499B (en) * | 2021-02-04 | 2024-02-02 | 芜湖楚睿智能科技有限公司 | Lithium battery health state and state of charge joint estimation method based on machine learning |
| CN113009349B (en) * | 2021-04-09 | 2024-01-05 | 哈尔滨工业大学 | Lithium ion battery health state diagnosis method based on deep learning model |
-
2021
- 2021-07-22 CN CN202110831688.8A patent/CN113740739B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110850298A (en) * | 2019-10-29 | 2020-02-28 | 上海交通大学 | Lithium battery SOH estimation method and system based on data driving |
| CN112269134A (en) * | 2020-09-10 | 2021-01-26 | 杭州电子科技大学 | Battery SOC and SOH joint estimation method based on deep learning |
| CN113093014A (en) * | 2021-03-31 | 2021-07-09 | 山东建筑大学 | Online collaborative estimation method and system for SOH and SOC based on impedance parameters |
Non-Patent Citations (2)
| Title |
|---|
| 张立佳 ; 徐国宁 ; 赵向阳 ; 杜晓伟 ; 周翔 ; .基于神经网络的老化锂电池SOC估算方法的研究.电源学报.2020,(第01期),全文. * |
| 王跃飞 ; 舒成才 ; 刘白隽 ; 孙召辉 ; .基于电池状态的车载铅酸电池输出在线控制方法.农业装备与车辆工程.2019,(第04期),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113740739A (en) | 2021-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11105861B2 (en) | Device and method for estimating battery resistance | |
| EP2629109B1 (en) | Electrical storage device | |
| CN103163480B (en) | The appraisal procedure of lithium battery health status | |
| CN104749533B (en) | A kind of health state of lithium ion battery estimation on line method | |
| CN102998623B (en) | The estimation on line method and system of storage battery charge state | |
| CN110061531B (en) | Energy storage battery equalization method | |
| CN104391252B (en) | Automobile lead-acid battery health state detection method | |
| CN102749588B (en) | Method for fault diagnosis on basis of storage battery state of charge (SOC) and state of health (SOH) | |
| KR101498760B1 (en) | Apparatus and method of estimating state of charging for battery, and battery management system using the same | |
| CN109358293B (en) | Lithium ion battery SOC estimation method based on IPF | |
| CN111025168A (en) | Battery health state monitoring device and battery state of charge intelligent estimation method | |
| WO2008065910A1 (en) | Accumulator failure detecting device, accumulator failure detecting method, accumulator failure detecting program, and computer-readable recording medium containing the accumulator failure detecting program | |
| CN102230953A (en) | Method for predicting left capacity and health status of storage battery | |
| CN110895310A (en) | SOC (state of charge) estimation system of lithium iron phosphate battery | |
| CN102944844A (en) | Device for emergency ignition and fault diagnosis of vehicular lead-acid storage battery and method | |
| CN112924866B (en) | Method and device for detecting capacity retention rate, vehicle and storage medium | |
| CN113646650A (en) | Battery state estimating apparatus | |
| Lu et al. | Modeling discharge characteristics for predicting battery remaining life | |
| CN103529397A (en) | Method for estimating electric quantity of battery and system for managing electric quantity of battery | |
| CN113740739B (en) | Vehicle-mounted starting maintenance-free lead-acid storage battery residual life prediction method | |
| CN113884922A (en) | Battery internal short circuit quantitative diagnosis method based on voltage and electric quantity outlier coefficient | |
| CN114167301A (en) | Power battery evaluation method based on real vehicle data of electric vehicle | |
| CN111071074A (en) | Electric vehicle optimized charging method combining big data and BMS | |
| CN110780225B (en) | Battery pack internal short circuit detection method and device and electric automobile | |
| CN114035061B (en) | On-line estimation method and system for battery state of health of battery pack |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |