Method and device for predicting multiple peak power performance parameters of batteries

Abstract

The method and device for predicting multiple peak power performance parameters of a storage battery provided by the present invention belong to the field of battery detection technology, and include: constructing a peak power function of a target battery based on a charged voltage function of the target battery and a physical model of the target battery; and determining a battery voltage function during the charge and discharge process of the target battery; predicting performance parameters of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, the performance parameters including peak power or charge and discharge time. The method and device for predicting multiple peak power performance parameters of a storage battery provided by the present invention non-destructively and accurately predict performance parameters such as the maximum allowable power of the battery at each stage, and can reduce the workload of peak power detection, as well as provide technical support for peak power in actual battery management systems, designs, vehicle designs, and road use, and provide a reference for systematic and scientific experimental verification.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a method and device for predicting multiple peak power performance parameters of a battery. Background Art

[0002] With the development of new energy technologies, the electric vehicle industry has developed rapidly, and production and sales have increased rapidly. Secondary battery technologies such as lithium-ion batteries, which are closely related to it, have gradually become a popular research direction. With the increasing requirements for high-rate and fast-charging technologies, the importance of battery peak power continues to increase, and it has great application prospects.

[0003] In actual driving, the vehicle often accelerates for a period of time, stops for a short time, and then continues to accelerate. The power prediction in these stages is of great significance to the design of the entire vehicle.

[0004] However, there is currently no method to predict the maximum permissible power in these phases. Summary of the invention

[0005] The method and device for predicting multiple peak power performance parameters of a battery provided by the present invention are used to solve the defect in the prior art that there is no method for predicting the maximum allowable power in the stage of accelerating for a period of time, stopping for a short period of time and then continuing to accelerate during driving, and realize non-destructive and accurate prediction of performance parameters such as the maximum allowable power of the battery in each stage, which can reduce the workload of peak power detection, provide technical support for peak power in actual battery management systems, designs, vehicle design and road use, and provide a reference basis for systematic and scientific experimental verification.

[0006] The present invention provides a method for predicting multiple peak power performance parameters of a battery, comprising:

[0007] Based on the charged voltage function of the target battery and the physical model of the target battery, construct the peak power function of the target battery; and determine the battery voltage function of the target battery during the charging and discharging process;

[0008] Based on the peak power function, the battery voltage function, and the battery state function of the target battery, a performance parameter of the target battery is predicted, where the performance parameter includes peak power or charge and discharge time.

[0009] According to a method for predicting multiple peak power performance parameters of a storage battery provided by the present invention, before constructing the peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery, the method further includes:

[0010] Determine voltage stability values ​​of a sample battery at multiple different states of charge;

[0011] Fitting a charge voltage curve according to each charge state and a voltage stability value corresponding to each charge state;

[0012] The charging voltage function is determined according to the charging voltage curve.

[0013] According to a method for predicting multiple peak power performance parameters of a storage battery provided by the present invention, before predicting the performance parameters of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, the method further includes:

[0014] Based on the physical model of the target battery, performing RPT tests on a plurality of sample batteries to determine the relationship between the internal resistance of the target battery and the temperature and state of charge;

[0015] The battery state function is constructed according to the relationship between the internal resistance, temperature and state of charge of the target battery.

[0016] According to a method for predicting multiple peak power performance parameters of a storage battery provided by the present invention, the peak power function of the target battery is constructed based on the charged voltage function of the target battery and the physical model of the target battery, including:

[0017] Based on the physical model of the target battery, a relationship between the peak power and the state of charge of the target battery is determined by using multiple calculation methods of the discharge capacity within a preset charge and discharge time;

[0018] The peak power function is constructed according to the charged voltage function and the relationship between the peak power and the state of charge of the target battery.

[0019] According to a method for predicting multiple peak power performance parameters of a battery provided by the present invention, the step of determining a battery voltage function during the charging and discharging process of the target battery comprises:

[0020] During each discharge process of the target battery, determining an initial state of charge and a cutoff state of charge of the target battery, and an actual voltage at the end of charge and discharge;

[0021] Determining a cutoff voltage corresponding to each cutoff state of charge in the charge voltage function;

[0022] The battery voltage function is constructed according to each actual voltage and a cut-off voltage corresponding to each actual voltage.

[0023] According to a method for predicting multiple peak power performance parameters of a battery provided by the present invention, the performance parameters of the target battery are predicted based on the peak power function, the battery voltage function, and the battery state function of the target battery, including:

[0024] Based on the peak power function, the battery voltage function, and the battery state function of the target battery, constructing a peak discharge power charge and discharge time function of the target battery;

[0025] Determining the charge and discharge time of the target battery according to the peak discharge power charge and discharge time function and the peak power; or,

[0026] The peak power of the target battery is determined according to the peak discharge power charge and discharge time function and the charge and discharge time.

[0027] The present invention also provides a device for predicting multiple peak power performance parameters of a battery, comprising:

[0028] A construction module, configured to construct a peak power function of the target battery based on a charged voltage function of the target battery and a physical model of the target battery; and to determine a battery voltage function during a charge and discharge process of the target battery;

[0029] A prediction module is used to predict the performance parameters of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, wherein the performance parameters include peak power or charge and discharge time.

[0030] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting multiple peak power performance parameters of a storage battery as described in any one of the above is implemented.

[0031] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for predicting multiple peak power performance parameters of a storage battery as described in any one of the above is implemented.

[0032] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for predicting multiple peak power performance parameters of a storage battery as described in any one of the above is implemented.

[0033] The method and device for predicting multiple peak power performance parameters of a battery provided by the present invention establish a relationship function between the performance parameters of the battery and easily measurable parameters such as voltage and charge, and construct a simple and effective peak power prediction model, thereby non-destructively and accurately predicting performance parameters such as the maximum allowable power of the battery at each stage. This can reduce the workload of peak power detection, and provide technical support for peak power in actual battery management systems, designs, vehicle design and road use, and provide a reference for systematic and scientific experimental verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 This is one of the flow charts of the method for predicting multiple peak power performance parameters of a battery provided by the present invention;

[0036] Figure 2 This is the second flow chart of the method for predicting multiple peak power performance parameters of a battery provided by the present invention;

[0037] Figure 3 It is a structural schematic diagram of a device for predicting multiple peak power performance parameters of a battery provided by the present invention;

[0038] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Existing technologies can achieve online estimation of the maximum allowable power, but they require more initial parameters and a more complex model.

[0041] The battery power state (SOP) is the maximum power that the battery can input and output under different conditions. This parameter reflects the battery's ability to withstand charging and discharging power. It is of great significance for the rational use of batteries, avoiding abuse such as overcharging and over-discharging, evaluating battery life, and maximizing the vehicle's braking energy recovery capabilities.

[0042] The three major mainstream testing methods are the Hybrid Pulse Power Characteristic (HPPC) test, the Japan Electric Vehicle Society (JEVS) method, and the Electric Vehicle Battery Test Procedures Manual. The peak power values ​​obtained by these testing methods are not the actual peak power.

[0043] The actual intrinsic peak power can be detected using the constant power method, but this method requires multiple tests of the power value, and each test requires a lot of manpower and material resources. Not only may the performance of the battery itself be damaged during repeated tests, but it will also lead to low detection efficiency.

[0044] If a more accurate prediction value of the battery peak power can be obtained through various battery parameters, the practical application value of this method will be greatly improved, and the detection efficiency can be greatly improved.

[0045] At present, peak power prediction methods include the Partnership for a New Generation of Vehicles (PNGV) composite pulse method and the power calculation method based on the change of state of charge (SOC). All of them have certain shortcomings, such as being too simple and ill-considered, insufficiently accurate, or using complex models, which are not suitable for basic prediction before detection, and cannot directly predict the maximum allowable power during multiple consecutive charging and discharging processes. They are not of sufficient reference value for the design of the entire vehicle and are not practical enough.

[0046] In order to realize the prediction method of peak power under different states, the present invention provides a continuous multiple peak power prediction method based on easily measurable parameters and instantaneous voltage and resistance changes.

[0047] Combine the following Figure 1-Figure 4 The present invention describes a method and device for predicting multiple peak power performance parameters of a battery provided by an embodiment of the present invention.

[0048] Figure 1 FIG. 1 is one of the flow charts of the method for predicting multiple peak power performance parameters of a battery provided by the present invention, such as Figure 1 As shown, including but not limited to the following steps:

[0049] First, in step S1, based on the charged voltage function of the target battery and the physical model of the target battery, a peak power function of the target battery is constructed; and a battery voltage function of the target battery during the charging and discharging process is determined.

[0050] The target battery may be a lithium-ion battery.

[0051] In the process of battery modeling, the battery can be equivalent to a physical model of an ideal voltage source U and a resistor R in series, where the resistor R is the internal resistance of the battery.

[0052] Optionally, before constructing the peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery, the method further includes:

[0053] Determine voltage stability values ​​of a sample battery at multiple different states of charge;

[0054] Fitting a charge voltage curve according to each charge state and a voltage stability value corresponding to each charge state;

[0055] The charging voltage function is determined according to the charging voltage curve.

[0056] The sample battery may be of the same model or the same batch as the target battery and have the same parameters and properties.

[0057] First, charge each sample battery according to the charging method specified for the sample battery, and leave it alone for 2 hours after being fully charged to allow the voltage of the sample battery to stabilize.

[0058] Secondly, the sample battery is discharged with a current of I1 to 5% of the measured discharge capacity, i.e., 5% SOC, and allowed to stand for 2 hours. After the voltage stabilizes, the voltage stability value OCV is measured and recorded. The sample battery is repeatedly discharged with a current of I1 to 5% of the measured discharge capacity, and allowed to stand for 2 hours. The measured voltage stability value OCV is recorded until the sample battery is fully discharged. The voltage stability values ​​OCV under multiple SOCs are measured; wherein I1 is numerically equal to the discharge capacity of the sample battery.

[0059] Wherein, state of charge (SOC) = (remaining power / rated power) × 100%; voltage stability value OCV = open circuit voltage (OCV).

[0060] Next, according to the voltage stability value OCV at each SOC obtained by detection, the SOC-OCV curve is fitted by polynomial fitting or exponential fitting, and then the charge voltage function corresponding to the batch of batteries is obtained: SOC=h(OCV), and OCV=f(SOC).

[0061] According to the method for predicting multiple peak power performance parameters of a storage battery provided by the invention, before the actual detection of the battery, the established lithium-ion single battery is charged and discharged for a certain period of time, and after a short rest, a charge and discharge measurement experiment is carried out using a battery with the same parameters and properties, so as to fit the relationship curve between the capacity and voltage of the batch of batteries, thereby obtaining the charged voltage function, which provides a basis for the accurate prediction of the performance parameters of the lithium-ion battery under different states.

[0062] Optionally, constructing the peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery includes:

[0063] Based on the physical model of the target battery, a relationship between the peak power and the state of charge of the target battery is determined by using multiple calculation methods of the discharge capacity within a preset charge and discharge time;

[0064] The peak power function is constructed according to the charged voltage function and the relationship between the peak power and the state of charge of the target battery.

[0065] After obtaining the charge voltage function, the peak power function of the target battery is constructed based on the charge voltage function SOC=h(OCV) of the target battery and the physical model UR of the target battery:

[0066]

[0067] Wherein, P is the peak power, unit: watt (W); U is the average voltage of the target battery during the charging and discharging process; t1 is the charging and discharging time of the target battery; OCV1 is the open circuit voltage of the target battery corresponding to the initial SOC1; U aim is the required cut-off voltage, unit: volt (V); Cap is the actual capacity of the battery, unit: ampere-hour (AH); SOC1 is the initial state of charge of the battery, SOC1 = h(OCV1).

[0068] The peak power P may be a fixed value of the target battery in a certain state and may vary according to the SOC.

[0069] The method for predicting multiple peak power performance parameters of a battery provided by the present invention fully considers the changes in battery voltage and resistance during the charging and discharging process, and has higher accuracy and better reliability than a single parameter prediction model; it can also be combined with the actual operation of the vehicle, taking into account the easily measurable parameters of the battery (OCV, R, etc.), and exploring the maximum power allowed during multiple consecutive charging and discharging of the battery, providing a reference for the design of the output power of the vehicle, that is, combining the changes in voltage and resistance after the battery is charged and discharged, and using the obtained peak power model. The maximum power allowed for the batch of batteries during multiple consecutive charging and discharging processes is obtained.

[0070] Optionally, determining a battery voltage function of the target battery during a charging and discharging process includes:

[0071] During each discharge process of the target battery, determining an initial state of charge and a cutoff state of charge of the target battery, and an actual voltage at the end of charge and discharge;

[0072] Determining a cutoff voltage corresponding to each cutoff state of charge in the charge voltage function;

[0073] The battery voltage function is constructed according to each actual voltage and a cut-off voltage corresponding to each actual voltage.

[0074] On the other hand, after charging and leaving the target battery at rest, the target battery is charged and discharged again to conduct a study on the prediction of the change in OCV.

[0075] For example, after the battery is discharged, the SOC state changes from the initial charge state SOC1 to the cut-off charge state SOC2, which correspond to the initial voltage OCV1 and the cut-off voltage OCV2 in the stable state. Considering that the battery is in an unstable state when it is stationary for a short time, the SOC2 state can be reached quickly, but the value of the voltage OCV will gradually rise over time, and it cannot rise to the cut-off voltage OCV2 corresponding to SOC2 in a short time. Among them, OCV2 can be directly obtained from the charge voltage function according to SOC2, and the actual voltage that OCV can reach within a certain stationary time t2 is recorded as OCV3. After the voltage rebounds for a certain time t2 after discharge but has not stabilized, and the OCV value when it needs to continue discharging is recorded as OCV3. This time t2 is determined according to actual needs, for example, the time interval between two accelerator pedal presses of a car.

[0076] Note that ΔOCV=OCV2-OCV3, and based on all measured voltages OCV3 and cut-off voltages OCV2, a battery voltage function during the discharge process of the target battery is constructed, as shown below:

[0077] ΔOCV=k(I,R,t2);

[0078] Wherein, I is the average current of the target battery during the charging and discharging process; R is the internal resistance of the target battery; and t2 is the static time of the target battery after the charging and discharging are terminated.

[0079] According to the method for predicting multiple peak power performance parameters of a battery provided by the invention, by constructing a relationship function between the theoretical voltage and the measured voltage of the battery after charging and discharging, taking into account the influence of the polarization voltage and the polarization internal resistance, and utilizing the mathematical relationship between the actual SOC and the actual OCV and the theoretical OCV, accurate modeling of the battery voltage can be achieved. Combined with the established peak power prediction model for batteries from the same batch, the maximum power allowed during multiple consecutive charge and discharge processes of batteries from the same batch is predicted and explored, and the results are corrected, providing a basis for predicting the performance parameters of the target battery.

[0080] Optionally, before predicting the performance parameter of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, the method further includes:

[0081] Based on the physical model of the target battery, performing RPT tests on a plurality of sample batteries to determine the relationship between the internal resistance of the target battery and the temperature and state of charge;

[0082] The battery state function is constructed according to the relationship between the internal resistance, temperature and state of charge of the target battery.

[0083] A rational performance tester (RPT) is performed on multiple sample batteries to obtain multiple sets of DC resistance, temperature and SOC data of the sample batteries.

[0084] According to each set of DC resistance, temperature and SOC data, the relationship between the DC internal resistance R and the temperature T and SOC of the target battery can be determined.

[0085] According to the relationship between the DC internal resistance R, temperature T and SOC of the sample battery, the battery state function is constructed as follows:

[0086] R = g(SOC, T);

[0087] Wherein, R is the DC resistance of the target battery; T is the temperature of the target battery in degrees Celsius (°C); SOC is the state of charge of the target battery.

[0088] According to the method for predicting multiple peak power performance parameters of the battery provided by the present invention, the internal resistance of the battery at different SOCs and the voltage after short-time static charging and discharging are combined to reduce the use of complex models, and cleverly use different calculation methods of discharge capacity to establish the relationship between the charge and discharge current and easy-to-measure parameters (current voltage, cut-off voltage, charge and discharge time), thereby establishing an accurate prediction method for the actual intrinsic peak power of batteries with different SOCs under a multi-parameter system, providing strong theoretical and data support for battery testing, BMS design, vehicle design, etc.

[0089] Further, in step S2, based on the peak power function, the battery voltage function, and the battery state function of the target battery, performance parameters of the target battery are predicted, and the performance parameters include peak power or charge and discharge time.

[0090] By substituting the battery voltage function and the battery state function into the peak power function, the relationship between the peak power and the charge and discharge time of the target battery can be obtained, and then the charge and discharge time can be obtained from the known peak power, or the peak power can be obtained from the known charge and discharge time.

[0091] Optionally, predicting the performance parameter of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery includes:

[0092] Based on the peak power function, the battery voltage function, and the battery state function of the target battery, constructing a peak discharge power charge and discharge time function of the target battery;

[0093] Determining the charge and discharge time of the target battery according to the peak discharge power charge and discharge time function and the peak power; or,

[0094] The peak power of the target battery is determined according to the peak discharge power charge and discharge time function and the charge and discharge time.

[0095] Specifically, using the charge and discharge characteristics of lithium-ion batteries, the changes in battery voltage and internal resistance during a short period of rest, the actual OCV3 = OCV2-ΔOCV and battery state function R = g(SOC, T) after the battery is charged and discharged for a short period of rest are substituted into the peak power function of the battery. In the above, we can get the multi-parameter model of peak discharge power charge and discharge time function, which can convert the performance parameters into the expression of easy-to-measure parameters. The details are as follows:

[0096] (P, t1) = h (SOC, R) = h [f (OCV), g (T)];

[0097] Based on the peak power-charge and discharge time function, the peak power prediction within a certain charge and discharge time t1, or the maximum charge and discharge and minimum charge and discharge time prediction that can be achieved under a certain discharge power can be obtained.

[0098] The method for predicting multiple peak power performance parameters of a battery provided by the present invention builds a simple and effective peak power prediction model by establishing a relationship function between the performance parameters of the battery and easily measurable parameters such as voltage and charge, thereby performing non-destructive prediction of the performance of the battery. This can reduce the workload of peak power detection, and provide technical support for peak power in actual battery management systems (BMS), design, vehicle design and road use, and provide a reference for systematic and scientific experimental verification.

[0099] Figure 2 FIG. 2 is a flow chart of the method for predicting multiple peak power performance parameters of a battery provided by the present invention. Figure 2 As shown, including:

[0100] By performing activation and capacity calibration tests on the same batch of batteries, we can ensure that the basic parameters and basic properties of the same batch of batteries are consistent, thereby improving the credibility of subsequent data fitting and data verification;

[0101] Three sample batteries with the same basic properties are selected to establish a simplified physical model of lithium-ion batteries. The physical model is composed of an ideal voltage source OCV and a DC resistor R in series, where R is the internal resistance of the battery. The battery is tested for SOC-OCV. Every time the SOC is reduced by 5%, the OCV is measured. The specific test method is as follows:

[0102] First, charge each sample battery according to the charging method specified for the sample battery and let it stand for 2 hours after being fully charged;

[0103] Secondly, the sample battery is discharged with a current of I1 to 5% of the measured discharge capacity, left to stand for 2 hours, and the voltage stabilization value OCV is measured and recorded after the voltage stabilizes. The sample battery is repeatedly discharged with a current of I1 to 5% of the measured discharge capacity, left to stand for 2 hours, and the measured OCV value is recorded until the sample battery is fully discharged. The OCV values ​​under multiple SOCs are measured; among which I1 is numerically equal to the discharge capacity of the sample battery.

[0104] Next, based on the OCV data at each SOC obtained by the test, the SOC-OCV curve is fitted by using polynomial fitting or exponential fitting, and then the charge voltage function corresponding to the batch of batteries is obtained: SOC=h(OCV), and OCV=f(SOC).

[0105] The three sample batteries were used to perform RPT tests to obtain the relationship between the DC internal resistance R and the temperature T and SOC, which is the battery state function: R=g(SOC, T).

[0106] The corresponding calculation formula is obtained and verified. Data fitting relationship curve fitting degree r 2≥0.99, and the verification error of the test data shall not exceed 10%. The linear regression fit is used to measure the degree of fit between the data predicted by the linear regression and the real data, and is expressed as r 2 Indicates that r 2 The closer it is to 1, the better the fit, that is, the better the linear regression model.

[0107] Under different SOC states, the relationship between P, t, OCV, and temperature T is explored. By using different calculation methods of discharge capacity within a certain charge and discharge time t, the peak power function of the battery between the peak power P and the easily measured parameters SOC, R, etc. is established:

[0108]

[0109] Wherein, P is the peak power, unit: watt (W); U is the average voltage of the target battery during the charging and discharging process; t is the charging and discharging time of the target battery; OCV1 is the open circuit voltage of the target battery corresponding to the initial SOC1; U aim is the required cut-off voltage, unit: volt (V); Cap is the actual capacity of the battery, unit: ampere-hour (AH); SOC1 is the initial state of charge of the battery;

[0110] The average voltage U during the charge and discharge process is the value that needs to be adjusted in the experiment, the internal resistance R is the value that needs to be determined in the experiment, and the peak power P is the solution value.

[0111] According to whether the measured peak power is consistent with the peak power predicted in the peak power function of the battery, determine whether the selection of U is appropriate. The measured peak power can be determined for the constant power detection method, that is, the actual intrinsic peak power, which is more accurate and has more practical reference and application significance than other detection methods.

[0112] Under certain temperature conditions, such as 25℃±2℃, the target battery is charged and left to stand, and then discharged to conduct OCV change prediction research. For example, after the battery is discharged, the SOC state changes from SOC1 to SOC2, which correspond to OCV1 and OCV2 respectively in the stable state.

[0113] Taking into account that the battery is in an unstable state when it is stationary for a short time, the SOC2 state can be reached quickly, and the OCV value will gradually recover over time, but it cannot recover to OCV2 corresponding to SOC2 in a short time, among which, OCV2 can be directly obtained according to SOC2 in the charge voltage function, and the detection voltage that OCV can reach within a certain stationary time t2 is recorded as OCV3.

[0114] Note that ΔOCV=OCV2-OCV3. In the actual detection process, by shortening the sampling time to accurately monitor the changes in OCV during the standing time after charging and discharging, it can be determined that ΔOCV is related to the charging and discharging current, the internal resistance of the battery (ohmic internal resistance, polarization internal resistance), and the standing time t2.

[0115] By fitting the experimentally measured data, the battery voltage function is obtained:

[0116] ΔOCV=k(I,R,t2);

[0117] Wherein, I is the average current of the target battery during the charging and discharging process; R is the internal resistance of the target battery; and t2 is the static time of the target battery after the charging and discharging are terminated.

[0118] Next, the battery voltage function is verified.

[0119] Finally, the actual OCV3 = OCV2-ΔOCV after the battery is charged and discharged for a short period of time is substituted into the peak power function of the battery, and the peak discharge power charge and discharge time function can be obtained as follows:

[0120] (P, t1) = h (SOC, R) = h [f (OCV), g (T)];

[0121] Based on the peak power-charge and discharge time function, the peak power prediction within a certain charge and discharge time t1, or the maximum charge and discharge and minimum charge and discharge time prediction that can be achieved under a certain discharge power can be obtained. The mathematical model required for the peak power-charge and discharge time function is relatively simple, and the parameters are easy to obtain, which can be applied to computing systems with low computing power.

[0122] According to the method for predicting multiple peak power performance parameters of a battery provided by the present invention, a mathematical model is fitted for the relationship between the battery's state of charge SOC and voltage U, the change in internal resistance R, and the change in polarization voltage during the static process; a simplified physical model is used, combined with a certain battery discharge capacity, and with power, voltage, internal resistance, etc. as initial conditions, an equation is established and solved, and the equation can be used to predict the maximum allowable power during continuous multiple accelerations and continuous multiple fast charging processes through actual test verification and adjustment, so as to achieve rapid and accurate prediction of the maximum power state during continuous multiple charging and discharging processes of lithium-ion batteries, which can greatly save the time and manpower costs of the constant power detection method, overcome the difficulty that the constant power detection method used for the actual peak power requires a lot of manpower and material resources, and predict the maximum allowable power during continuous multiple charging and discharging processes while minimizing damage to battery performance, thereby ensuring the speed and accuracy of measurement, and providing good theoretical and technical support for the design of the output power of the entire vehicle and the control of power during the actual operation of the entire vehicle, which is of great significance to automobile companies and the battery detection industry.

[0123] The multiple peak power performance parameter prediction device of a battery provided by the present invention is described below. The multiple peak power performance parameter prediction device of a battery described below and the multiple peak power performance parameter prediction method of a battery described above can be referenced to each other.

[0124] Figure 3 Schematic diagram of the structure of the device for predicting multiple peak power performance parameters of a battery provided by the present invention. Figure 3 As shown, including:

[0125] A construction module 301 is used to construct a peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery; and determine the battery voltage function of the target battery during the charging and discharging process;

[0126] The prediction module 302 is used to predict the performance parameters of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, where the performance parameters include peak power or charge and discharge time.

[0127] During the operation of the device, the construction module 301 constructs the peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery; and determines the battery voltage function of the target battery during the charging and discharging process; the prediction module 302 predicts the performance parameters of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, and the performance parameters include peak power or charging and discharging time.

[0128] The device for predicting multiple peak power performance parameters of a battery provided by the present invention builds a simple and effective peak power prediction model by establishing a relationship function between the performance parameters of the battery and easily measurable parameters such as voltage and charge, thereby performing non-destructive prediction of the performance of the battery. This can reduce the workload of peak power detection, provide technical support for peak power in actual battery management systems, design, vehicle design and road use, and provide a reference for systematic and scientific experimental verification.

[0129] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute a method for predicting multiple peak power performance parameters of a storage battery, the method comprising: constructing a peak power function of the target battery based on a charged voltage function of the target battery and a physical model of the target battery; and determining a battery voltage function during the charge and discharge process of the target battery; and predicting performance parameters of the target battery based on the peak power function, the battery voltage function and the battery state function of the target battery, the performance parameters including peak power or charge and discharge time.

[0130] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0131] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the multiple peak power performance parameter prediction methods of the battery provided by the above methods, the method including: constructing a peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery; and determining the battery voltage function of the target battery during the charging and discharging process; based on the peak power function, the battery voltage function, and the battery state function of the target battery, predicting the performance parameters of the target battery, the performance parameters including peak power or charging and discharging time.

[0132] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the multiple peak power performance parameter prediction methods of a storage battery provided by the above-mentioned methods, the method comprising: constructing a peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery; and determining the battery voltage function of the target battery during the charging and discharging process; based on the peak power function, the battery voltage function, and the battery state function of the target battery, predicting the performance parameters of the target battery, the performance parameters including peak power or charging and discharging time.

[0133] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0135] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting multiple peak power performance parameters of a battery, characterized in that: include: Constructing a peak power function of the target battery based on a charged voltage function of the target battery and a physical model of the target battery; and determining a battery voltage function during the charging and discharging process of the target battery; Based on the peak power function, the battery voltage function, and the battery state function of the target battery, predicting a performance parameter of the target battery, the performance parameter including peak power or charge and discharge time; The expression of the peak power function is: ； Wherein, P is the peak power; U is the average voltage of the target battery during the charging and discharging process; is the charge and discharge time of the target battery; Cap is the actual capacity of the battery; is the charging voltage function; is the open circuit voltage of the target battery at the initial battery power state; is the required cut-off voltage; R is the internal resistance of the target battery; The step of determining a battery voltage function during a charging and discharging process of the target battery includes: During each discharge process of the target battery, determine the initial state of charge and the cut-off state of charge of the target battery, as well as the actual voltage at the end of charging and discharging; determine the cut-off voltage corresponding to each cut-off state of charge in the charge voltage function; construct the battery voltage function according to each actual voltage and the cut-off voltage corresponding to each actual voltage; The expression of the battery voltage function is: ； Where I is the average current of the target battery during charging and discharging; The static time of the target battery after the charging and discharging is terminated; The construction process of the battery status function is: Based on the physical model of the target battery, performing RPT tests on multiple sample batteries to determine the relationship between the internal resistance of the target battery and the temperature and state of charge; constructing the battery state function according to the relationship between the internal resistance of the target battery and the temperature and state of charge; The predicting the performance parameter of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery includes: Based on the peak power function, the battery voltage function, and the battery state function of the target battery, a peak discharge power charge and discharge time function of the target battery is constructed; according to the peak discharge power charge and discharge time function and the peak power, the charge and discharge time of the target battery is determined; or, according to the peak discharge power charge and discharge time function and the charge and discharge time, the peak power of the target battery is determined.

2. The method for predicting multiple peak power performance parameters of a battery according to claim 1, characterized in that: Before constructing the peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery, the method further includes: Determine voltage stability values ​​of a sample battery at multiple different states of charge; Fitting a charge voltage curve according to each charge state and a voltage stability value corresponding to each charge state; The charging voltage function is determined according to the charging voltage curve.

3. The method for predicting multiple peak power performance parameters of a battery according to claim 1, characterized in that: The step of constructing a peak power function of the target battery based on the charged voltage function of the target battery and the physical model of the target battery includes: Based on the physical model of the target battery, a relationship between the peak power and the state of charge of the target battery is determined by using multiple calculation methods of the discharge capacity within a preset charge and discharge time; The peak power function is constructed according to the charged voltage function and the relationship between the peak power and the state of charge of the target battery.

4. A device for predicting multiple peak power performance parameters of a battery, characterized in that: include: A construction module, configured to construct a peak power function of the target battery based on a charged voltage function of the target battery and a physical model of the target battery; and determining a battery voltage function during the charging and discharging process of the target battery; A prediction module, configured to predict a performance parameter of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery, wherein the performance parameter includes peak power or charge and discharge time; The expression of the peak power function is: ； Wherein, P is the peak power; U is the average voltage of the target battery during the charging and discharging process; is the charge and discharge time of the target battery; Cap is the actual capacity of the battery; is the charging voltage function; is the open circuit voltage of the target battery at the initial battery power state; is the required cut-off voltage; R is the internal resistance of the target battery; The construction module is used to determine the initial state of charge and the cut-off state of charge of the target battery and the actual voltage at the end of charging and discharging during each discharge process of the target battery; determine the cut-off voltage corresponding to each cut-off state of charge in the charge voltage function; and construct the battery voltage function according to each actual voltage and the cut-off voltage corresponding to each actual voltage; The battery voltage function is: ； Where I is the average current of the target battery during charging and discharging; The static time of the target battery after the charging and discharging is terminated; The construction process of the battery status function is: Based on the physical model of the target battery, performing RPT tests on multiple sample batteries to determine the relationship between the internal resistance of the target battery and the temperature and state of charge; constructing the battery state function according to the relationship between the internal resistance of the target battery and the temperature and state of charge; The prediction module is used to construct a peak discharge power charge and discharge time function of the target battery based on the peak power function, the battery voltage function, and the battery state function of the target battery; determine the charge and discharge time of the target battery according to the peak discharge power charge and discharge time function and the peak power; or determine the peak power of the target battery according to the peak discharge power charge and discharge time function and the charge and discharge time.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for predicting multiple peak power performance parameters of a battery as described in any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting multiple peak power performance parameters of a battery as described in any one of claims 1 to 3 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting multiple peak power performance parameters of a battery as described in any one of claims 1 to 3 is implemented.

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