Lead-acid battery performance prediction method and related device

Abstract

The invention relates to a lead-acid battery performance prediction method, which comprises the following steps of: acquiring charge and discharge characteristic data of a lead-acid battery, solving RC values of the lead-acid battery in an equivalent circuit model under different conditions, fitting to form curves of RC parameters respectively changing along with temperature, current and SOC (State of Charge), calculating RC values in any state, and forming a preliminary simulation model; a test method of some existing actual measurement results of the battery is converted into the initial SOC, temperature and current conditions of the battery, the initial SOC, temperature and current conditions are input into the preliminary simulation model to obtain a performance prediction result, the prediction result is compared with the actual measurement results, and if the deviation is within 5%, the prediction result serves as a final simulation model; and according to the test requirements of the lead-acid battery of the model, analyzing the test method to determine the initial temperature, the SOC state and the charge and discharge current requirements, and inputting the initial temperature, the SOC state and the charge and discharge current requirements into the simulation model corresponding to the lead-acid battery of the model to obtain battery performance prediction data. The prediction method is well matched with an experiment result, the test time cost and the resource cost can be greatly reduced, and meanwhile battery waste cannot be caused.

Description

Technical Field

[0001] The invention belongs to the technical field of lead-acid batteries, and in particular relates to a method for predicting performance of lead-acid batteries. Background Art

[0002] Lead-acid batteries have gone through more than a hundred years of development since their invention, and currently the battery performance is still judged by laboratory charge and discharge tests. This method is not only time-consuming and labor-intensive, but also occupies test resources, and some life tests will also cause battery waste. At the same time, traditional charge and discharge test methods are highly dependent on test standards. Once there is a difference in test standards, it is necessary to select the battery again for retesting. Summary of the invention

[0003] The purpose of the present invention is to overcome the above-mentioned deficiencies in the prior art and to provide a lead-acid battery performance prediction method to effectively reduce the resource and time costs of battery performance detection.

[0004] The purpose of the present invention is achieved by the following technical solution: A method for predicting the performance of a lead-acid battery comprises the following steps:

[0005] 1.1. Collect the charge and discharge characteristic data of the lead-acid battery to be tested, and use the mathematical expression of the second-order equivalent circuit model to solve the RC value (R 0 , R 1 , R 2 , C 1 , C 2 parameter value);

[0006] 1.2. Fit the RC values ​​obtained in each state in 1.1 by using the difference method and the least square method to form curves showing the changes of RC parameters with temperature, current and SOC, and calculate the RC value in any state; combine the mathematical expression of the second-order equivalent circuit model to form a preliminary simulation model of the lead-acid battery; convert some existing test methods of the battery into the initial SOC, temperature and current conditions of the battery required for simulation, input them into the preliminary simulation model to obtain the performance prediction results, compare the prediction results with the measured results, and if the deviation is within 5%, use it as the final simulation model;

[0007] 1.3. According to the test requirements of this type of lead-acid battery, the test method is analyzed to determine the initial temperature, SOC state, and current requirements for charging and discharging, and they are input into the simulation model corresponding to this type of lead-acid battery. The mathematical expression of the second-order equivalent model is solved to obtain the battery performance prediction data.

[0008] Step 1.1 specifically includes:

[0009] 1.1.1. Considering the lead-acid battery as a second-order equivalent circuit, in the second-order equivalent circuit model:

[0010] Voltage source: Use Uoc to represent the open circuit voltage OCV of the battery;

[0011] Ohmic internal resistance: Use R 0 Indicates the battery electrode material, electrolyte, diaphragm resistance and contact resistance of each part;

[0012] RC network: through polarization internal resistance R 1 , R 2 and polarization capacitance C 1 , C 2 Describe the dynamic characteristics of batteries, including polarization properties and diffusion effects;

[0013] The mathematical expression of the second-order equivalent circuit is:

[0014]

[0015] Where Vo represents the battery operating voltage, U 1 , U 2 Represents R 1 C 1 Networking and R 2 C 2 The voltage across the network, I represents the total current, and t represents time;

[0016] Therefore, its operating voltage is: The equivalent internal resistance is:

[0017] 1.1.2. Obtain the SOC-OCV data and internal resistance R of the lead-acid battery under different temperature T conditions through the following steps. 0 -SOC data and HPPC test data under different temperatures and current excitation conditions, etc.:

[0018] a) Fully charge the battery whose performance needs to be predicted, and then place it at the specified test temperature T for 24 hours, and record the voltage Uoc and internal resistance R of the battery at 100% SOC. 0 , temperature T;

[0019] b) Let stand for 1 min, discharge at a constant current I for 10 s, let stand for 120 s, record the voltage Vo(t), then recharge the discharged capacity at a constant voltage of 16 V and a current limit of 1 / 10C, ​​let stand for 1+28 min; then repeat this step with other currents I from small to large until all the discharge currents are completed; the discharge current I is 1 / 20C, 15A, 25A, 1C, 100A, 5C, 10C;

[0020] c) Discharge to the next SOC state at a current rate of 0.05C, let it stand for 5 hours, and record the SOC voltage Uoc and internal resistance R at this time 0 ; Repeat step b; the next SOC state percentage is 5% less than the previous SOC state percentage; SOC is calculated using the ampere-hour integration method: SOC(t) = SOC 0 +1 / Cn*∫ 0 t I(t)*dt Formula (6)

[0021] d) Repeat steps a to c to obtain test data at different temperatures T, SOC, and current I; the temperature T is selected as -40°C, -30°C, -18°C, 0°C, 25°C, 40°C, and 75°C;

[0022] e) Perform curve fitting on the recorded data to obtain T-SOC-OCV curve, T-SOC-R 0 curve and HPPC curve;

[0023] 1.1.3. Determine Uoc based on T-SOC-OCV curve, and 0 Curve determination R 0 , apply formula (4) to fit and solve the data of each charging or discharging section in each step b of the HPPC test, and obtain the R in the RC equivalent circuit model under the temperature, SOC and current excitation conditions. 1 , R 2 , C 1 , C 2 Repeat this step to calculate the R in the RC equivalent circuit model of the lead-acid battery under different temperatures, different SOCs, and different current excitation conditions. 0 , R 1 , R 2 , C 1 , C 2 Parameters.

[0024] Step 1.2 specifically includes:

[0025] 1.2.1. Use the difference method and the least square method to fit the RC values ​​under various conditions obtained in 1.1.

[0026] 1.2.2. Form the curves of RC parameters changing with temperature, current and SOC respectively, and calculate the Uoc and RC values ​​in any state;

[0027] That is: Uoc = f 0 (T,SOC);

[0028] R 0 =f 1 (T,SOC);

[0029] R 1 =f 2 (T,I,SOC);

[0030] R 2 =f 3 (T,I,SOC);

[0031] C 1 =f 4 (T,I,SOC);

[0032] C 2 =f 5 (T,I,SOC);

[0033] Use formula (4) and input the above parameter values ​​to form a preliminary simulation model Vo(t);

[0034] 1.2.3. Convert some existing test methods of the battery into the initial SOC, temperature and current conditions of the battery required by the simulation model and input them into the preliminary simulation model Vo(t) to obtain the performance prediction results, which are compared with the actual test results of the battery performance. If the deviation is within 5%, it will be used as the final simulation model.

[0035] Step 1.2.1 specifically includes:

[0036] First, the acquired parameter data is fitted, and the linear region and nonlinear region are determined according to the parameter change trend;

[0037] In the linear region, the difference method is directly applied to solve the parameter value between two adjacent data points;

[0038] In the nonlinear region, the least squares method is applied to every three adjacent data points to solve the parameter value, which will result in two solved parameter values ​​at each position, and then the arithmetic mean of these two parameter values ​​is calculated as the parameter fitting value at that position.

[0039] In step 1.2.3, if the deviation between the predicted result and the measured result exceeds 5%, the RC parameters need to be optimized:

[0040] h) According to the data of each charging or discharging segment in the measured data, formula (4) is applied to fit and solve, and the R in the equivalent circuit model under the test conditions (temperature, SOC, charge / discharge current) is obtained. 0 , R 1 , R 2 , C 1 , C 2 Parameters;

[0041] i) Compare the RC parameters with the RC parameters under the same condition in the original model, and use parameter estimation methods to manually adjust or nonlinear least squares fitting to obtain new RC parameters; then use the new RC parameters to perform performance prediction; if the prediction deviation is still large, repeat this step until the prediction deviation is within 5%;

[0042] By fine-tuning and optimizing the Uoc and RC parameters, the parameter curve is made smoother and the prediction accuracy of the simulation model is improved, until the deviation between the predicted value of the fitted model and the actual measured value of the battery performance is within 5%, and the RC parameters used in the simulation model are reasonable; the RC parameters after optimization and fitting are solidified and packaged to form the final simulation model.

[0043] Step 1.3 specifically includes:

[0044] 1.3.1. Analyze the battery test method and convert it into information that can be recognized by the simulation model, including but not limited to the battery initial SOC, temperature, current conditions, and output data frequency, and input this information into the corresponding parameters or control requirements of the simulation model;

[0045] 1.3.2. Run the simulation model to predict battery performance; the simulation model will output a data curve based on the conditions input in 1.3.1.

[0046] The present invention also provides a lead-acid battery performance prediction device, comprising:

[0047] The data preparation module is used to collect the charge and discharge characteristic data of the lead-acid battery to be tested, and solve the RC value of the equivalent circuit model of the lead-acid battery under different conditions using the mathematical expression of the second-order equivalent circuit model;

[0048] The model building module is used to fit the RC values ​​obtained in each state in 1.1 by using the difference method and the least square method to form a curve of the RC parameters changing with temperature, current, and SOC, and calculate the RC value in any state; combine the mathematical expression of the second-order equivalent circuit model to form a preliminary simulation model of the lead-acid battery; convert some existing test methods of the battery's actual measurement results into the battery's initial SOC, temperature, and current conditions required for simulation, input them into the preliminary simulation model to obtain performance prediction results, compare the prediction results with the actual measurement results, and use them as the final simulation model if the deviation is within 5%;

[0049] The performance prediction module is used to analyze the test method according to the test requirements of this model of lead-acid battery to determine the initial temperature, SOC state and current requirements for charging and discharging, and input them into the simulation model corresponding to this model of lead-acid battery, and solve them according to the mathematical expression of the second-order equivalent model to obtain battery performance prediction data.

[0050] It also includes an optimization module for optimizing RC parameters when the deviation between the predicted result and the measured result exceeds 5%, so that the deviation between the fitted model predicted value and the measured value of the battery performance is within 5%.

[0051] Another aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the lead-acid battery performance prediction method described in the first aspect when executing the computer program.

[0052] Another aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lead-acid battery performance prediction method described in the first aspect.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] 1) The present invention provides a method for predicting the performance of a lead-acid battery based on a simulation model. Taking into account the problem of large differences in performance prediction under different initial conditions of batteries, RC parameter acquisition and calibration are performed for different temperatures, different SOC states, and different current excitation conditions. The prediction results of the simulation model are compared with the actual test results of the battery to optimize the fitting of the RC parameters, thereby improving the prediction accuracy of the simulation model and reducing the deviation from the actual test.

[0055] 2) The present invention can effectively reduce battery testing resources and battery costs. The present invention has low computational cost and high model universality, which can greatly reduce the time cost and test resource cost of testing, and will not cause waste of batteries. New standard tests can be performed at any time, especially when developing new battery products. The original "scheme → trial assembly → testing → judgment → scheme → ... → judgment → meet demand" cycle business process is simplified to "scheme → simulation → judgment → scheme → ... → judgment → meet demand" cycle process, which saves a lot of time and resource costs in the "trial assembly-testing" cycle process, creates opportunity value, makes the product more competitive in the market, and has a good application prospect.

[0056] 3) The simulation model established in the present invention is in good agreement with the experimental results, and can provide RC parameters of the equivalent circuit model for BMS algorithm development, which helps to improve the BMS's evaluation and prediction of the life, reliability and performance of lead-acid batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 It is a schematic diagram of the product development process after the present invention is used;

[0059] Figure 2 It is a schematic diagram of the original product development process;

[0060] Figure 3 It is a schematic diagram of a process of establishing a simulation model according to an embodiment of the present invention;

[0061] Figure 4 is a schematic diagram of a second-order equivalent circuit in an equivalent circuit model of an embodiment of the present invention;

[0062] Figure 5 is a schematic diagram of a SOC-OCV curve of a lead-acid battery provided in an embodiment of the present invention;

[0063] Figure 6 Schematic diagram of the HPPC curve of the lead-acid battery provided in an embodiment of the present invention;

[0064] Figure 7 : is the C20 discharge curve of the lead-acid battery simulation model according to an embodiment of the present invention;

[0065] Figure 8 This is a 25A discharge curve of the lead-acid battery simulation model of an embodiment of the present invention at -20°C and 50% SOC. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without carrying out creative work are within the scope of protection of the present invention.

[0067] The experimental methods or test methods described in the following examples are conventional methods unless otherwise specified. The techniques, methods and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification.

[0068] A first aspect of the present invention provides a lead-acid battery performance prediction method, comprising the following steps:

[0069] 1.1. Based on the equivalent circuit principle of the battery in the circuit, the charge and discharge characteristic data of the lead-acid battery are collected, and the RC value of the equivalent circuit model of the lead-acid battery under different conditions is solved using the mathematical expression of the second-order equivalent model.

[0070] 1.2. Use the difference method and the least square method to fit the RC values ​​under various states obtained in 1.1, and form curves showing the changes of RC parameters with temperature, current, and SOC, so as to calculate the RC value under any state. Thus, combined with the mathematical expression of the second-order equivalent circuit model, a preliminary lead-acid battery equivalent circuit simulation model is formed. Then, the prediction results of the simulation model are compared with the actual test results of the battery, and the RC parameters are optimized and fitted to improve the prediction accuracy of the simulation model and form the final simulation model.

[0071] 1.3. According to a battery test requirement, the test method is analyzed to determine the initial temperature, SOC state, and current requirements for charging and discharging, and input them into the simulation model corresponding to the battery. The mathematical expression of the second-order equivalent model is solved to obtain the battery performance prediction data.

[0072] Step 1.1 specifically includes:

[0073] 1.1.1. Principle of lead-acid battery equivalent circuit model: Lead-acid battery is a chemical power source. The interface of different phases in the battery will produce a double layer. The double layer in the electrochemical reaction can be regarded as a "leaky capacitor". Considering the internal resistance of each component, connection resistance and electrolyte resistance in the battery, the lead-acid battery can be regarded as a second-order equivalent circuit. Figure 4 , in the second-order equivalent circuit model:

[0074] Voltage source: Use Uoc to represent the open circuit voltage OCV of the battery.

[0075] Ohmic internal resistance: R0 is used to represent the battery electrode material, electrolyte, diaphragm resistance and contact resistance of each part.

[0076] RC network: The dynamic characteristics of the battery, including polarization characteristics and diffusion effects, are described by the polarization internal resistance R1, R2 and the polarization capacitors C1, C2.

[0077] The mathematical expression of the second-order equivalent circuit is:

[0078]

[0079] Where Vo represents the operating voltage of the battery, U1 and U2 represent the voltages across the R1C1 network and the R2C2 network respectively, I represents the total current, and t represents time.

[0080] Therefore, its working voltage formula is: Equivalent internal resistance formula:

[0081] 1.1.2. Obtain the SOC-OCV data and internal resistance R of the lead-acid battery under different temperature T conditions through the following steps. 0 -SOC data and HPPC test data under different temperatures and current excitation conditions, etc.:

[0082] a) Fully charge the battery whose performance needs to be predicted, and then place it at the specified test temperature T for 24 hours, and record the voltage Uoc and internal resistance R of the battery at 100% SOC. 0 , temperature T;

[0083] b) Stand for 1 minute, discharge at a constant current I for 10 seconds, stand for 120 seconds, record the voltage Vo(t), then recharge the discharged capacity at a constant voltage of 16V and a current limit of 1 / 10C, ​​stand for 1+28 minutes; then repeat this step with other currents I in sequence (from small to large) until all discharge currents are completed. The discharge current I is 1 / 20C, 15A, 25A, 1C, 100A, 5C, 10C.

[0084] c) Discharge to the next SOC state at a current rate of 0.05C, let it stand for 5 hours, and record the SOC voltage Uoc and internal resistance R at this time 0 ; Repeat step b; if the initial SOC is 100%, the next SOC should be 95%, then 90%, ..., until 5% SOC, 0% SOC. SOC is calculated using the ampere-hour integration method: SOC(t) = SOC 0 +1 / Cn*∫ 0 t I(t)*dt Formula (6)

[0085] d) Repeat steps a to c to obtain test data under different temperatures T, SOC, and current I. The temperature T is selected as -40°C, -30°C, -18°C, 0°C, 25°C, 40°C, and 75°C.

[0086] e) The recorded data are subjected to curve fitting to obtain the T-SOC-OCV curve ( Figure 5 )、T-SOC-R 0 Curve and HPPC curve ( Figure 6 ).

[0087] 1.1.3. Determine Uoc based on T-SOC-OCV curve, and 0 Curve determination R 0 , apply formula (4) to fit and solve the data of each charging or discharging section in each step b of the HPPC test, and obtain the R in the RC equivalent circuit model under the temperature, SOC and current excitation conditions. 1 , R 2 , C 1 , C 2 Repeat this step to calculate the R in the RC equivalent circuit model of the lead-acid battery under different temperatures, different SOCs, and different current excitation conditions. 1 , R 2 , C 1 , C 2 Parameters.

[0088] Step 1.2 specifically includes:

[0089] 1.2.1. Use the difference method and the least square method to fit the RC values ​​under various conditions obtained in 1.1:

[0090] First, the acquired parameter data is fitted, and the linear region and nonlinear region are determined according to the parameter change trend;

[0091] In the linear region, the difference method is directly applied to solve the parameter value between two adjacent data points;

[0092] In the nonlinear region, the least squares method is applied to every three adjacent data points to solve the parameter value, which will result in two solved parameter values ​​at each position, and then the arithmetic mean of these two parameter values ​​is calculated as the parameter fitting value at that position.

[0093] 1.2.2. Using the method of calculating the fitting value in 1.2.1, a curve is formed showing the change of RC parameters with temperature, current and SOC, so as to calculate the Uoc and RC values ​​in any state.

[0094] That is: Uoc = f 0 (T,SOC);

[0095] R 0 =f 1 (T,SOC);

[0096] R 1 =f 2 (T,I,SOC);

[0097] R 2 =f 3 (T,I,SOC);

[0098] C 1 =f 4 (T,I,SOC);

[0099] C 2 =f 5 (T,I,SOC);

[0100] Use formula (4) and input the above parameter values ​​to form a preliminary simulation model Vo(t).

[0101] 1.2.3. Convert some existing test methods of the battery into the initial SOC, temperature and current conditions of the battery required by the simulation model, input them into the preliminary simulation model, obtain the performance prediction results, and compare them with the actual test results of the battery. If the deviation is within 5%, it will be used as the final simulation model.

[0102] If the deviation between the predicted result and the measured result is large, the RC parameters need to be optimized:

[0103] h) According to the data of each charging or discharging segment in the measured data, formula (4) is applied to fit and solve, and the R in the equivalent circuit model under the test conditions (temperature, SOC, charge / discharge current) is obtained. 0 , R 1 , R 2 , C 1 , C 2 Parameters.

[0104] i) Compare the RC parameters with the RC parameters under the same conditions in the original model, and use parameter estimation methods to manually adjust or nonlinear least squares fitting to obtain new RC parameters. Then use the new RC parameters to predict performance. If the prediction deviation is still large, repeat this step until the prediction deviation meets expectations.

[0105] By fine-tuning and optimizing the Uoc and RC parameters, the parameter curve is made smoother and the prediction accuracy of the simulation model is improved, until the deviation between the predicted value of the fitted model and the actual measured value of the battery performance is within 5%, and the RC parameters used in the simulation model are reasonable; the RC parameters after optimization and fitting are solidified and packaged to form the final simulation model.

[0106] The RC parameters obtained by the HPPC method are generally more accurate, which is also the advantage of this method. Parameter optimization fitting is only a way to remedy the situation when some prediction deviations are abnormal.

[0107] Step 1.3 specifically includes:

[0108] 1.3.1. Analyze the battery test method and convert it into information that can be recognized by the simulation model, including but not limited to the battery's initial SOC, temperature, current conditions, output data frequency, etc., and input this information into the corresponding parameters or control requirements of the simulation model.

[0109] 1.3.2. Run the simulation model to predict battery performance. The model will output a data curve based on the conditions input in 1.3.1.

[0110] Now we need to predict the C20 capacity of a 12V45Ah battery and the performance of the battery at 50% SOC at -20℃ with 25A discharge and recording the discharge voltage per second:

[0111] The simulation model of this type of battery was established according to the above steps 1.1 to 1.2.

[0112] Analysis of C20 test method: initial SOC is 100%, temperature is 25℃, current I=0.05C=0.05*45=2.25A, output data frequency has no special requirements, so it can be set to record every 5 minutes. Then input the information into the simulation model and start running, and then the predicted data of the performance will appear, and its data curve is shown in Figure 7 .

[0113] Analysis of the test method of 50% SOC at -20℃ with 25A discharge: initial SOC is 50%, temperature is -20℃, current I=25A, and output data frequency is one record every 1s. Then input this information into the simulation model and start running, and then the predicted data of the performance will appear, and its data curve is shown in Figure 8 .

[0114] The prediction deviations of the two properties are both less than 5% through data analysis with actual tests, indicating that the model prediction accuracy is high. When the two properties are tested later, the corresponding simulation model prediction data can be directly used.

[0115] The embodiment of the present invention also provides a lead-acid battery performance prediction device, comprising:

[0116] The data preparation module is used to collect the charge and discharge characteristic data of the lead-acid battery to be tested, and solve the RC value of the equivalent circuit model of the lead-acid battery under different conditions using the mathematical expression of the second-order equivalent circuit model;

[0117] The model building module is used to fit the RC values ​​obtained in each state in 1.1 by using the difference method and the least square method to form a curve of the RC parameters changing with temperature, current, and SOC, and calculate the RC value in any state; combine the mathematical expression of the second-order equivalent circuit model to form a preliminary simulation model of the lead-acid battery; convert some existing test methods of the battery's actual measurement results into the battery's initial SOC, temperature, and current conditions required for simulation, input them into the preliminary simulation model to obtain performance prediction results, compare the prediction results with the actual measurement results, and use them as the final simulation model if the deviation is within 5%;

[0118] The performance prediction module is used to analyze the test method according to the test requirements of this model of lead-acid battery to determine the initial temperature, SOC state and current requirements for charging and discharging, and input them into the simulation model corresponding to this model of lead-acid battery (solving them according to the mathematical expression of the second-order equivalent model) to obtain battery performance prediction data.

[0119] It also includes an optimization module for optimizing RC parameters when the deviation between the predicted result and the measured result exceeds 5%, so that the deviation between the fitted model predicted value and the measured value of the battery performance is within 5%.

[0120] Another aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the lead-acid battery performance prediction method described in the first aspect when executing the computer program.

[0121] Another aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lead-acid battery performance prediction method described in the first aspect.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A lead-acid battery performance prediction method, characterized in that: The following steps are involved: 1.

1. Collect the charge and discharge characteristic data of the lead-acid battery to be tested, and use the mathematical expression of the second-order equivalent circuit model to solve the RC value of the equivalent circuit model of the lead-acid battery under different conditions; 1.

2. Fit the RC values ​​obtained in each state in 1.1 by using the difference method and the least square method to form curves showing the changes of RC parameters with temperature, current and SOC, and calculate the RC value in any state; combine the mathematical expression of the second-order equivalent circuit model to form a preliminary simulation model of the lead-acid battery; convert some existing test methods of the battery into the initial SOC, temperature and current conditions of the battery required for simulation, input them into the preliminary simulation model to obtain the performance prediction results, compare the prediction results with the measured results, and if the deviation is within 5%, use it as the final simulation model; 1.

3. According to the test requirements of this type of lead-acid battery, the test method is analyzed to determine the initial temperature, SOC state and current requirements for charging and discharging, and they are input into the simulation model corresponding to this type of lead-acid battery to obtain battery performance prediction data.

2. The lead-acid battery performance prediction method according to claim 1, characterized in that: Step 1.1 Specific include: 1.1.

1. Considering the lead-acid battery as a second-order equivalent circuit, in the second-order equivalent circuit model: Voltage source: Use Uoc to represent the open circuit voltage OCV of the battery; Ohmic internal resistance: R0 is used to represent the contact resistance of battery electrode materials, electrolyte, diaphragm resistance and various parts; RC network: describes the dynamic characteristics of the battery through polarization internal resistance R1, R2 and polarization capacitors C1, C2, including polarization characteristics and diffusion effects; The mathematical expression of the second-order equivalent circuit is: Where Vo represents the battery operating voltage, U1 and U2 represent the voltages across the R1C1 network and the R2C2 network respectively, I represents the total current, and t represents time; Therefore, its operating voltage is: The equivalent internal resistance is: 1.1.

2. Obtain the SOC-OCV data, internal resistance R0-SOC data of the lead-acid battery under different temperature T conditions, and HPPC test data under different temperature and current excitation conditions through the following steps: a) Fully charge the battery whose performance needs to be predicted, and then place it at the specified test temperature T for 24 hours, and record the voltage Uoc, internal resistance R0, and temperature T of the battery at 100% SOC; b) Let stand for 1 min, discharge at a constant current I for 10 s, let stand for 120 s, record the voltage Vo(t), then recharge the discharged capacity at a constant voltage of 16 V and a current limit of 1 / 10C, ​​let stand for 1+28 min; then repeat this step with other currents I from small to large until all the discharge currents are completed; the discharge current I is 1 / 20C, 15A, 25A, 1C, 100A, 5C, 10C; c) Discharge to the next SOC state with a current rate of 0.05C, let it stand for 5 hours, and record the voltage Uoc and internal resistance R0 of the SOC at this time; repeat step b; the next SOC state percentage is 5% less than the previous SOC state percentage; SOC is calculated using the ampere-hour integration method: SOC(t) = SOC0 + 1 / Cn*∫0 t I(t)*dt Formula (6) d) Repeat steps a to c to obtain test data at different temperatures T, SOC, and current I; the temperature T is selected as -40°C, -30°C, -18°C, 0°C, 25°C, 40°C, and 75°C; e) performing curve fitting on the recorded data to obtain a T-SOC-OCV curve, a T-SOC-R0 curve and a HPPC curve; 1.1.

3. Determine Uoc according to the T-SOC-OCV curve, determine R0 according to the T-SOC-R0 curve, apply formula (4) to fit and solve the data of each charging section or discharging section in each step b of the HPPC test, and obtain the parameters of R1, R2, C1, and C2 in the RC equivalent circuit model under the temperature, SOC, and current excitation conditions; repeat this step to calculate the parameters of R0, R1, R2, C1, and C2 in the RC equivalent circuit model of the lead-acid battery under different temperatures, different SOCs, and different current excitation conditions.

3. The lead-acid battery performance prediction method according to claim 1, characterized in that: Step 1.2 specifically includes: 1.2.

1. Use the difference method and the least square method to fit the RC values ​​under various conditions obtained in 1.

1. 1.2.

2. Form the curves of RC parameters changing with temperature, current and SOC respectively, and calculate the Uoc and RC values ​​in any state; That is: Uoc = f0 (T, SOC); R0=f1(T,SOC); R1=f2(T,I,SOC); R2=f3(T,I,SOC); C1=f4(T,I,SOC); C2=f5(T,I,SOC); Use formula (4) and input the above parameter values ​​to form a preliminary simulation model Vo(t); 1.2.

3. Convert some existing test methods of the battery into the initial SOC, temperature and current conditions of the battery required by the simulation model and input them into the preliminary simulation model Vo(t) to obtain the performance prediction results, and compare them with the actual test results of the battery performance. If the deviation is within 5%, it will be used as the final simulation model.

4. The lead-acid battery performance prediction method according to claim 3, characterized in that: Step 1.2.1 specifically includes: First, the acquired parameter data is fitted, and the linear region and nonlinear region are determined according to the parameter change trend; In the linear region, the difference method is directly applied to solve the parameter value between two adjacent data points; In the nonlinear region, the least squares method is applied to every three adjacent data points to solve the parameter value, which will result in two solved parameter values ​​at each position, and then the arithmetic mean of these two parameter values ​​is calculated as the parameter fitting value at that position.

5. The lead-acid battery performance prediction method according to claim 3, characterized in that: In step 1.2.3, if the deviation between the predicted result and the measured result exceeds 5%, the RC parameters need to be optimized: h) according to the data of each charging section or discharging section in the measured data, formula (4) is applied to perform fitting and solving, and the parameters R0, R1, R2, C1, and C2 in the equivalent circuit model under the test conditions are obtained; i) Compare the RC parameters with the RC parameters under the same condition in the original model, and use parameter estimation methods to manually adjust or nonlinear least squares fitting to obtain new RC parameters; then use the new RC parameters to perform performance prediction; if the prediction deviation is still large, repeat this step until the prediction deviation is within 5%; By fine-tuning and optimizing the Uoc and RC parameters, the parameter curve is made smoother and the prediction accuracy of the simulation model is improved, until the deviation between the predicted value of the fitted model and the actual measured value of the battery performance is within 5%, and the RC parameters used in the simulation model are reasonable; the RC parameters after optimization and fitting are solidified and packaged to form the final simulation model.

6. The lead-acid battery performance prediction method according to claim 1, characterized in that: Step 1.3 specifically includes: 1.3.

1. Analyze the battery test method and convert it into information that can be recognized by the simulation model, including but not limited to the battery initial SOC, temperature, current conditions, and output data frequency, and input this information into the corresponding parameters or control requirements of the simulation model; 1.3.

2. Run the simulation model to predict battery performance; the simulation model will output a data curve based on the conditions input in 1.3.

1.

7. A lead-acid battery performance prediction device, characterized in that: include: The data preparation module is used to collect the charge and discharge characteristic data of the lead-acid battery to be tested, and solve the RC value of the equivalent circuit model of the lead-acid battery under different conditions using the mathematical expression of the second-order equivalent circuit model; The model building module is used to fit the RC values ​​obtained in each state in 1.1 by using the difference method and the least square method to form a curve of the RC parameters changing with temperature, current, and SOC, and calculate the RC value in any state; combine the mathematical expression of the second-order equivalent circuit model to form a preliminary simulation model of the lead-acid battery; convert some existing test methods of the battery's actual measurement results into the battery's initial SOC, temperature, and current conditions required for simulation, input them into the preliminary simulation model to obtain performance prediction results, compare the prediction results with the actual measurement results, and use them as the final simulation model if the deviation is within 5%; The performance prediction module is used to analyze the test method according to the test requirements of this model of lead-acid battery to determine the initial temperature, SOC state and current requirements for charging and discharging, and input them into the simulation model corresponding to this model of lead-acid battery to obtain battery performance prediction data.

8. The lead-acid battery performance prediction device according to claim 7, characterized in that: It also includes an optimization module for optimizing RC parameters when the deviation between the predicted result and the measured result exceeds 5%, so that the deviation between the fitted model prediction result and the measured result of battery performance is within 5%.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for predicting the performance of a lead-acid battery as described in any one of claims 1 to 6 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the lead-acid battery performance prediction method according to any one of claims 1 to 6 is implemented.

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