Battery health state estimation method and device, electronic equipment and storage medium

Abstract

The application provides a battery state of health estimation method and device, electronic equipment and storage medium, comprising: at a first time in a battery standby time, obtaining a first current and a first terminal voltage of the battery, wherein the first current and the first terminal voltage are generated based on a reference direct current pulse signal; determining a test battery state of health SOH of the battery based on the first current and the first terminal voltage; at a second time in a battery operation time, obtaining a second current and a second terminal voltage of the battery; determining an estimated SOH of the battery based on the second current, the second terminal voltage and a prediction algorithm; in a case where it is determined that a condition of adjusting a prediction algorithm parameter is met based on the test SOH and the estimated SOH, adjusting the prediction algorithm parameter to obtain a target prediction algorithm, so as to estimate the SOH of the battery based on the target prediction algorithm.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, electronic device, and storage medium for estimating the state of health of a battery. Background Technology

[0002] Battery State of Health (SOH) refers to the percentage of the battery's current capacity (charged or discharged) to its nominal capacity under certain conditions. SOH reflects battery aging and capacity decay. According to IEEE standards, when a power battery's capacity drops to 80%, the battery is considered aged and unusable and should be replaced promptly. Therefore, SOH needs to be estimated. Related technologies estimate SOH using the battery's internal resistance, but this method lacks precision. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a method, apparatus, electronic device, and storage medium for estimating battery health status.

[0004] This application provides a method for estimating battery health status, including:

[0005] At the first moment during the battery standby time, the first current and the first terminal voltage of the battery are acquired, wherein the first current and the first terminal voltage are generated based on a reference DC pulse signal;

[0006] Based on the first current and the first terminal voltage, the test SOH of the battery is determined;

[0007] At a second moment during the battery's operating time, the second current and the second terminal voltage of the battery are acquired, wherein the first moment and the second moment are within a preset time interval, and the second current and the second terminal voltage are generated during the operation of the battery.

[0008] The estimated SOH of the battery is determined based on the second current, the second terminal voltage, and the prediction algorithm.

[0009] If the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, the prediction algorithm parameters are adjusted to obtain the target prediction algorithm, and the SOH of the battery is estimated based on the target prediction algorithm.

[0010] In some embodiments, when the battery is detected to be in a standby state, a control signal is generated, the control signal being used to control the battery to generate a reference DC pulse signal;

[0011] The control signal is sent to the battery to obtain the first current and the first terminal voltage of the battery.

[0012] In some embodiments, determining the test state of the battery based on the first current and the first terminal voltage includes:

[0013] Based on the first current and the first voltage, the first internal resistance of the battery is determined;

[0014] The test SOH of the battery is determined based on the first internal resistance.

[0015] In some embodiments, determining the test SOH of the battery based on the first internal resistance includes:

[0016] Obtain the initial internal resistance and the end-of-life internal resistance;

[0017] The first difference is determined based on the battery's initial internal resistance and the first internal resistance;

[0018] The second difference is determined based on the initial internal resistance of the battery and the internal resistance at the end of the battery's lifespan.

[0019] The test SOH of the battery is determined based on the first difference and the second difference.

[0020] In some embodiments, the method further includes:

[0021] Based on the tested SOH and estimated SOH, determine whether the conditions for adjusting the prediction algorithm parameters are met.

[0022] In some embodiments, determining whether the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH includes:

[0023] Determine the difference between the tested SOH and the estimated SOH;

[0024] Determine the relationship between the difference and the preset threshold;

[0025] Based on the size relationship, it is determined whether the conditions for adjusting the budget algorithm parameters are met. If it is determined that the conditions for adjusting the prediction algorithm parameters are not met, the prediction algorithm is determined as the target algorithm.

[0026] In some embodiments, the method further includes:

[0027] The first internal resistance is determined based on the first current and the first terminal voltage;

[0028] The target equivalent circuit model is determined based on the first internal resistance;

[0029] Update the internal resistance parameter of the battery to the internal resistance parameter corresponding to the target equivalent circuit model.

[0030] This application provides a device for estimating the state of health (SOH) of a battery, comprising:

[0031] The first acquisition module is used to acquire the first current and the first terminal voltage of the battery at a first moment during the battery standby time, wherein the first current and the first terminal voltage are generated based on a reference DC pulse signal;

[0032] The first determining module is used to determine the test SOH of the battery based on the first current and the first terminal voltage;

[0033] The second acquisition module is used to acquire the second current and the second terminal voltage of the battery at a second moment during the battery's operating time, wherein the first moment and the second moment are within a preset time interval, and the second current and the second terminal voltage are generated during the operation of the battery.

[0034] The second determining module is used to determine the estimated SOH of the battery based on the second current, the second terminal voltage, and the prediction algorithm.

[0035] An adjustment module is used to adjust the prediction algorithm parameters to obtain a target prediction algorithm when the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, so as to estimate the SOH of the battery based on the target prediction algorithm.

[0036] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs any of the above-described methods for estimating the state of battery health (SOH).

[0037] This application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement the battery health state (SOH) estimation method described in any of the above claims.

[0038] This application provides a method, apparatus, electronic device, and storage medium for estimating the state of battery health. It generates a first voltage and a first current using a DC pulse signal. At a first moment during battery standby time, the first voltage and first current are generated using a reference DC pulse signal, and a test state of health (SOH) is determined based on the first voltage and first current. At a second moment during battery operation, a second current and a second terminal voltage of the battery are acquired. The first and second moments are within a preset time interval, and an estimated SOH is determined based on the second current, second voltage, and a prediction algorithm. The prediction algorithm is optimized by testing and estimating the SOH to determine a target budget algorithm. The SOH is then estimated using the target budget algorithm, thereby improving the accuracy of SOH estimation. Attached Figure Description

[0039] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0040] Figure 1 A schematic diagram illustrating the implementation process of a battery health state estimation method provided in this application embodiment;

[0041] Figure 2 A schematic diagram of a battery health state estimation system provided in an embodiment of this application;

[0042] Figure 3 A schematic diagram of another battery health state estimation system provided in an embodiment of this application;

[0043] Figure 4 A schematic flowchart illustrating a test for determining the state of harmonics (SOH) of a battery, provided as an embodiment of this application;

[0044] Figure 5 A flowchart illustrating a reference DC pulse signal generation control instruction provided for an embodiment of this application;

[0045] Figure 6 This is a schematic diagram of the BMU signal detection process provided in an embodiment of this application;

[0046] Figure 7 A schematic diagram illustrating the implementation process of another SOH estimation method provided in this application embodiment;

[0047] Figure 8 A schematic diagram illustrating the implementation flow of a parameter optimization method for a prediction algorithm provided in an embodiment of this application;

[0048] Figure 9 A schematic diagram of a battery health status estimation device provided in an embodiment of this application;

[0049] Figure 10 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application.

[0050] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0053] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0055] To address the problems existing in related technologies, this application provides a method for estimating battery health status, which is applied to electronic devices. The battery health status estimation method provided in this application can be implemented by the processor of the electronic device calling program code, wherein the program code can be stored in a computer storage medium.

[0056] This application provides a method for estimating battery health status. Figure 1 This is a schematic diagram illustrating the implementation process of a battery health state estimation method provided in an embodiment of this application, as shown below. Figure 1 As shown, it includes:

[0057] Step S101: At the first moment during the battery standby time, acquire the first current and the first terminal voltage of the battery, wherein the first current and the first terminal voltage are generated based on a reference DC pulse signal.

[0058] In this embodiment, the battery may be a lithium battery, comprising a battery body and a battery management unit (BMU). In some embodiments, the battery may be an energy storage system. The inverter may include DC / AC or DC / DC converters. The standby time may be the time during which the battery is not charged or discharged. The first moment may be any moment within the standby time. In this embodiment, the electronic device may generate a control signal to cause the battery to generate a reference DC pulse signal. The BMU system may detect a first current and a first terminal voltage generated on the battery body, and then send the first current and the first terminal voltage to the electronic device. For example, the first current and the first terminal voltage may be sent to the controller of the electronic device.

[0059] Step S102: Based on the first current and the first terminal voltage, determine the test battery health state SOH of the battery.

[0060] In this embodiment, the test SOH of the battery can be determined by: determining the first internal resistance of the battery based on the first current and the first voltage; and determining the test SOH of the battery based on the first internal resistance. In this embodiment, the initial internal resistance and the end-of-life internal resistance can be obtained; a first difference can be determined based on the initial internal resistance and the first internal resistance; a second difference can be determined based on the initial internal resistance and the end-of-life internal resistance; and the test SOH corresponding to the target equivalent circuit model can be determined based on the first difference and the second difference.

[0061] Step S103: At a second moment during the battery's operating time, obtain the second current and the second terminal voltage of the battery.

[0062] In this embodiment, the second current and the second terminal voltage are generated during battery operation. In this embodiment, the battery operating time can be the battery charging and discharging time. The battery's BMU system can send the generated second current and second terminal voltage to the electronic device, allowing the electronic device to obtain the battery's second current and second terminal voltage. In this embodiment, by ensuring the first and second moments are within a preset time interval, which can be set based on experience, the accuracy of the calculation can be improved.

[0063] Step S104: Determine the estimated SOH of the battery based on the second current, the second terminal voltage, and the prediction algorithm.

[0064] In this embodiment of the application, the prediction algorithm can be a Kalman filter algorithm model, which can estimate the SOH during battery operation by using the second current, the second terminal voltage and the Kalman filter algorithm model.

[0065] Step S105: If the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, the prediction algorithm parameters are adjusted to obtain the target prediction algorithm, so as to estimate the SOH of the battery based on the target prediction algorithm.

[0066] In this embodiment, the difference between the tested SOH and the estimated SOH can be determined; the relationship between the difference and a preset threshold can be determined; if the relationship indicates that the difference is less than the preset threshold, it is determined that the condition for adjusting the prediction algorithm parameters is not met, and in this case, the prediction algorithm is the target prediction algorithm. If the relationship indicates that the difference is greater than or equal to the preset threshold, it is determined that the condition for adjusting the prediction algorithm parameters is met, and the prediction algorithm parameters are adjusted; the target prediction algorithm is determined based on the adjusted parameters.

[0067] This application provides a method for estimating the state of battery health. A first voltage and a first current are generated using a DC pulse signal. At the first moment of battery standby time, the first voltage and first current are generated using a reference DC pulse signal, and a test state of health (SOH) is determined based on the first voltage and first current. At the second moment of battery operation, a second current and a second terminal voltage of the battery are acquired. The first and second moments are within a preset time interval. An estimated SOH is determined based on the second current, the second voltage, and a prediction algorithm. The prediction algorithm is optimized by testing and estimating the SOH to determine a target prediction algorithm. The target prediction algorithm is then used to estimate the SOH, thereby improving the accuracy of SOH estimation.

[0068] Based on the foregoing embodiments, this application further provides a method for estimating battery health status, the method comprising:

[0069] Step S201: When the battery is detected to be in standby mode, a control signal is generated.

[0070] In this embodiment, the battery's current or voltage can be used to determine whether it is in standby mode. When in standby mode, the electronic device can generate a control signal and send it to the inverter, which can be either AC / DC or DC / DC. In this embodiment, a current control loop can be used in the inverter to add control functionality, enabling the battery to perform pulse charging and discharging. Figure 2 This is a schematic diagram of a battery health state estimation system provided in an embodiment of this application, as shown below. Figure 2 As shown, the controller of the electronic device is connected to the battery's BMS system, the controller of the electronic device is connected to a DC / DC converter, and the controller is connected to an AC / DC converter. Figure 3 A schematic diagram of another battery health state estimation system provided in this application embodiment is shown below. Figure 3As shown, the controller of the electronic device is connected to the AC / DC converter.

[0071] Step S202: Send the control signal to the battery.

[0072] In this embodiment of the application, when the battery receives a control signal, it can generate a reference DC pulse signal based on the control signal.

[0073] Step S203: Obtain the first current and the first terminal voltage of the battery, wherein the first current and the first terminal voltage are generated based on a reference DC pulse signal.

[0074] Step S204: Based on the first current and the first terminal voltage, determine the test battery health state SOH of the battery.

[0075] Step S205: At a second moment during the battery's operating time, the second current and the second terminal voltage of the battery are obtained, wherein the first moment and the second moment are within a preset time interval, and the second current and the second terminal voltage are generated during the operation of the battery.

[0076] Step S206: Determine the estimated SOH of the battery based on the second current, the second terminal voltage, and the prediction algorithm.

[0077] Step S207: If the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, the prediction algorithm parameters are adjusted to obtain the target prediction algorithm, so as to estimate the SOH of the battery based on the target prediction algorithm.

[0078] The SOH estimation method provided in this application provides that, when the battery is detected to be in standby mode, a control signal is generated to add a pulse test function to the converter, making the battery internal resistance test more convenient. It can be completed automatically during system operation intervals without the need for additional testing equipment, thus reducing costs and manpower requirements.

[0079] In some embodiments, step S102, "determining the test SOH of the battery based on the first internal resistance," can be achieved through the following steps. Figure 4 This application provides a schematic flowchart for determining the state of harmonics (SOH) of a battery through testing, as illustrated in the embodiments of this application. Figure 4 As shown:

[0080] Step S1021: Obtain the initial internal resistance and the end-of-life internal resistance of the battery.

[0081] In this embodiment, the initial internal resistance and the end-of-life internal resistance can be predetermined. The initial internal resistance can be represented by R. NEW This indicates that the end-of-life internal resistance can be represented by R.EOL express.

[0082] Step S1022: Determine the first difference based on the initial internal resistance of the battery and the first internal resistance.

[0083] In this embodiment of the application, the first internal resistance can be represented by R0(Ti).

[0084] Step S1023: Determine the second difference based on the initial internal resistance of the battery and the internal resistance at the end of the battery's life.

[0085] Step S1024: Determine the test SOH of the battery based on the first difference and the second difference.

[0086] In this embodiment of the application, the test SOH can be represented by SOH(Ti), and the calculation formula of SOH(Ti) is given by formula (1):

[0087] SOH(Ti)=R EOL -R0(Ti) / R EOL -R NEW (1);

[0088] The SOH estimation method provided in this application embodiment can determine the test SOH by detecting the first internal resistance of the battery during standby time, thereby adjusting the parameters of the prediction algorithm based on the test SOH and the estimated SOH to obtain the target prediction algorithm, and then estimating the SOH based on the target prediction algorithm.

[0089] In some embodiments, before step S105, "if the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, the prediction algorithm parameters are adjusted to obtain a target prediction algorithm, so as to estimate the SOH of the battery based on the target prediction algorithm," the method further includes:

[0090] Step S106: Based on the tested SOH and estimated SOH, determine whether the conditions for adjusting the prediction algorithm parameters are met.

[0091] In this embodiment of the application, step S106, "based on the tested SOH and the estimated SOH, determining whether the conditions for adjusting the prediction algorithm parameters are met," can be achieved through the following steps:

[0092] Step S1061: Determine the difference between the tested SOH and the estimated SOH.

[0093] In this embodiment of the application, the estimated SOH can be represented by SOH(ti).

[0094] Step S1062: Determine the relationship between the difference and the preset threshold.

[0095] In this embodiment of the application, the preset threshold can be represented by ξ, and ξ and SOH(t) can be compared. i )-SOH(T j The size of the ) determines the size relationship.

[0096] In this embodiment of the application, when ξ is greater than SOH(t) i )-SOH(T j If ξ is less than or equal to SOH(t), then the accuracy of the prediction algorithm in estimating SOH is considered to meet the requirements, and step S1063 is executed. i )-SOH(T j If ), then proceed to step S1064.

[0097] Step S1063: Determine that the conditions for adjusting the prediction algorithm parameters are not met.

[0098] This application attempts two methods: when it is determined that the conditions for adjusting the prediction algorithm parameters are not met, the prediction algorithm is determined to be the target prediction algorithm.

[0099] In this embodiment of the application, the parameters of the prediction algorithm are represented by f(k), which may include k1, k2, k3, k4, etc.

[0100] Step S1064: Determine if the conditions for adjusting the prediction algorithm parameters are met.

[0101] In this embodiment of the application, when adjusting the parameters of the prediction algorithm, one or more of them may be adjusted.

[0102] After completing step S1064, proceed to step S105.

[0103] In this embodiment of the application, when making adjustments, when the calculated SOH(t) i )-SOH(T j When ξ < ξ, the adjustment is complete, and the target prediction algorithm is then determined.

[0104] The SOH estimation method provided in this application uses the test SOH determined by the first current and the first terminal voltage as a standard to optimize the parameters of the prediction algorithm and obtain the target prediction algorithm. Based on the target prediction algorithm, the SOH is evaluated in real time, which can improve the estimation accuracy.

[0105] In some embodiments, after step S105, the method further includes:

[0106] Step S107: Obtain the third current and third terminal voltage of the battery.

[0107] Step S108: Calculate the SOH of the battery based on the third current, the third terminal voltage, and the target prediction algorithm.

[0108] In some embodiments, when performing step S102, the following steps may also be performed:

[0109] Step S106: Determine the first internal resistance based on the first current and the first terminal voltage.

[0110] In this embodiment, the first internal resistance can be calculated based on the first current and the first terminal voltage.

[0111] Step S107: Determine the target equivalent circuit model based on the first internal resistance.

[0112] In this embodiment, multiple equivalent circuit models can be pre-established, with different internal resistances for each model. A target equivalent circuit model can be determined from these models based on a second internal resistance.

[0113] Step S108: Update the internal resistance parameter of the battery to the internal resistance parameter corresponding to the target equivalent circuit model.

[0114] The SOH estimation method provided in this application provides that, by setting an equivalent circuit model, after determining the first internal resistance, a corresponding target equivalent circuit model is determined, and the internal resistance parameter is updated through the target equivalent circuit model. This allows the battery to update the internal resistance parameter intermittently during operation, and the latest internal resistance parameter is used for calculation in the next SOH estimation, thereby improving the estimation accuracy of SOH.

[0115] Based on the foregoing embodiments, this application further provides a method for estimating SOH, the method comprising:

[0116] Step S301: Add a battery internal resistance test function to the converter of the energy storage system (same as the battery in the above embodiment).

[0117] In this embodiment, the inverters currently used in energy storage batteries include both DC / AC and DC / DC types. Without changing the hardware configuration, a DC pulse discharge function is added through control, i.e., battery constant current control is adopted, and the constant current control time is ΔT. Figure 5 A flowchart illustrating a reference DC pulse signal generation control command is provided for an embodiment of this application, as shown below. Figure 5 As shown, the controller sends the reference pulse current to the current control loop, and the PWM generates switching control commands which are then sent to the converter.

[0118] In this embodiment, the BMU collects the current flowing through the battery module and the changes in terminal voltage. The BMU control chip (similar to the electronic device in the above embodiment) calculates the battery internal resistance parameter, and the battery internal resistance is obtained from the battery internal resistance parameter. Figure 6 This is a schematic diagram of the BMU signal detection process provided in the embodiments of this application, as shown below. Figure 6 As shown, by inputting a reference DC pulse to each battery module in the battery, the changes in current and voltage are obtained through the BMU corresponding to each battery module, and then the internal resistance parameters of the battery are calculated.

[0119] Step S302: Add a timed battery parameter detection process to the operation control logic of the energy storage converter. Perform the battery parameter detection in step 301 during system standby time (same as the battery standby time in the above embodiment) to obtain the battery internal resistance parameter R0(T). Based on different system standby times, obtain a series of battery internal resistances R0(Ti) for times T1, T2, T3…Tn. Calculate the battery's SOH_test (same as the test SOH in the above embodiment) based on the measured battery internal resistance parameter. The calculation of SOH_test is shown in the following formula:

[0120] SOH(Ti)=R EOL -R0(Ti) / R EOL -R NEW .

[0121] Step S303: During the operation of the energy storage system, the battery performs SOH estimation using an equivalent circuit model. For example, a battery SOH estimation based on the Kalman filter algorithm is used. After each system standby, the equivalent circuit model parameters are updated based on the battery internal resistance parameters obtained in step 302, and a battery SOH assessment is performed. This assessment can be performed in real time during the operation of the energy storage system, yielding a series of battery SOH(ti) for times t1, t2, t3, t4, ..., tn (same as the estimated SOH in the above embodiment).

[0122] Step S304: Based on the battery health status SOH_test, optimize the accuracy of the real-time battery health status assessment SOH(ti). When ti-Tj < ε, ε is an acceptable time interval, and the SOH assessment method is optimized within this time period. When SOH(t i )-SOH(T j If SOH(t) < ξ, then the accuracy of the real-time SOH assessment in step S303 is acceptable; when SOH(t) < ξ, then the accuracy of the real-time SOH assessment in step S303 is acceptable. i )-SOH(T j If ξ > ξ, then in step S303, the relevant parameters of the SOH estimation method are adjusted until the accuracy requirements are met, and then SOH estimation is performed based on the target prediction algorithm.

[0123] The State of Health (SOH) estimation method provided in this application adds a pulse test function to the converter, making battery internal resistance testing more convenient and enabling automatic completion during system operation intervals. No additional testing equipment is required, reducing costs and manpower needs. The parameters of the battery equivalent circuit model are updated intermittently during system operation, and the latest battery internal resistance parameters are used to assess the battery's SOH, improving assessment accuracy. Based on the SOH obtained from battery parameter testing, the parameters in the real-time SOH assessment algorithm are optimized to further improve assessment accuracy.

[0124] Based on the foregoing embodiments, this application further provides a method for estimating SOH. Figure 7 This is a schematic diagram illustrating the implementation process of another SOH estimation method provided in an embodiment of this application, as shown below. Figure 7 As shown, it includes:

[0125] Step S701: Determine whether the battery equivalent circuit parameters are missing.

[0126] In this embodiment, when the battery equivalent circuit parameters are not missing, step S705 is executed. When the battery equivalent circuit parameters are missing, step S702 is executed.

[0127] Step S702: Investigate the DC pulse test procedure.

[0128] Step S703: Determine the battery SOH(Tj).

[0129] Step S704, battery parameter update flag.

[0130] Step S705: Invoke the energy storage control program.

[0131] Step S706: Determine whether the energy storage system has started operating.

[0132] In this embodiment of the application, when the energy storage system is started and running, step S707 is executed; when the energy storage system is not started and running, step S702 is executed.

[0133] Step S707: Determine the battery SOH(ti).

[0134] Step S708: Determine if the battery parameters have been updated.

[0135] In this embodiment, the process ends when the battery parameters are not updated. When the battery parameters are updated, step S709 is executed.

[0136] Step S709, determine whether SOH(t) is present. i )-SOH(T j )<ξ.

[0137] In this embodiment of the application, when SOH(t)i )-SOH(T j When ) < ξ, execute step S411; when it is not SOH(t) i )-SOH(T j When ξ < ξ, proceed to step S410.

[0138] Step S710: Adjust the parameters of the battery monitoring status assessment method.

[0139] Step S711: Clear the battery parameter update flag.

[0140] Based on the foregoing embodiments, this application further provides a method for optimizing the parameters of a prediction algorithm. Figure 8 This is a schematic diagram illustrating the implementation flow of a parameter optimization method for a prediction algorithm provided in an embodiment of this application, as shown below. Figure 8 As shown, it includes:

[0141] Step S801: Obtain battery system measurement data.

[0142] Step S802: Evaluate using the SOH evaluation algorithm.

[0143] In this embodiment of the application, SOH_estimate(t) is obtained by evaluation using the SOH evaluation algorithm.

[0144] Step S803: Pulse test and calculation to obtain SOH test.

[0145] Step S804: Determine if the SOH deviation is greater than the set value.

[0146] In this embodiment of the application, when the value is not greater than the set value, step S801 is executed. When the value is greater than the set value, step S805 is executed.

[0147] Step S805: Determine that the time interval between the two sides is less than the set value.

[0148] If the time interval is less than the set value, proceed to step S806. If the two time intervals are not less than the set value, proceed to step S807.

[0149] Step S806: Adjust the algorithm parameters f(k)K1,k2,k3…….

[0150] Step S807, exception recording.

[0151] Based on the foregoing embodiments, this application provides a method and apparatus for estimating battery health status. The various modules and units included in the apparatus can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0152] This application provides a device for estimating battery health status. Figure 9 This is a schematic diagram of a battery health state estimation device provided in an embodiment of this application, as shown below. Figure 9 As shown, the battery health state estimation device 900 includes:

[0153] The first acquisition module 901 is used to acquire the first current and the first terminal voltage of the battery at a first moment during the battery standby time, wherein the first current and the first terminal voltage are generated based on a reference DC pulse signal;

[0154] The first determining module 902 is used to determine the test SOH of the battery based on the first current and the first terminal voltage;

[0155] The second acquisition module 903 is used to acquire the second current and the second terminal voltage of the battery at a second moment during the battery's operating time, wherein the first moment and the second moment are within a preset time interval, and the second current and the second terminal voltage are generated during the operation of the battery.

[0156] The second determining module 904 is used to determine the estimated SOH of the battery based on the second current, the second terminal voltage and the prediction algorithm;

[0157] The adjustment module 905 is used to adjust the prediction algorithm parameters to obtain a target prediction algorithm when the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, so as to estimate the SOH of the battery based on the target prediction algorithm.

[0158] In some embodiments, the pool health status estimation device 900 further includes:

[0159] The generation module is used to generate a control signal when the battery is detected to be in a standby state. The control signal is used to control the battery to generate a reference DC pulse signal.

[0160] A transmitting module is used to transmit the control to the battery in order to obtain the first current and the first terminal voltage of the battery.

[0161] In some embodiments, the first determining module 902 includes:

[0162] The first determining unit is configured to determine the first internal resistance of the battery based on the first current and the first voltage;

[0163] The second determining unit is used to determine the test SOH of the battery based on the first internal resistance.

[0164] In some embodiments, the second determining unit includes:

[0165] A sub-unit is used to obtain the initial internal resistance and end-of-life internal resistance of the battery;

[0166] The first determining subunit is used to determine a first difference based on the battery's initial internal resistance and the first internal resistance;

[0167] The second determining subunit is used to determine a second difference based on the initial internal resistance of the battery and the end-of-life internal resistance of the battery.

[0168] The third determining subunit is used to determine the test SOH of the battery based on the first difference and the second difference.

[0169] In some embodiments, the pool health status estimation device 900 further includes:

[0170] The judgment module is used to determine whether the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH.

[0171] In some embodiments, the determining module includes:

[0172] The third determining unit is used to determine the difference between the tested SOH and the estimated SOH;

[0173] The fourth determining unit is used to determine the relationship between the difference and the preset threshold.

[0174] The fifth determining unit is used to determine whether the conditions for adjusting the budget algorithm parameters are met based on the size relationship, wherein if the conditions for adjusting the prediction algorithm parameters are not met, the prediction algorithm is determined as the target prediction algorithm.

[0175] In some embodiments, the battery health state estimation device 900 further includes:

[0176] The fourth determining module is used to determine the first internal resistance based on the first current and the first terminal voltage;

[0177] The fifth determining module is used to determine the target equivalent circuit model based on the first internal resistance;

[0178] An update module is used to update the internal resistance parameter of the battery to the internal resistance parameter corresponding to the target equivalent circuit model.

[0179] It should be noted that, in the embodiments of this application, if the above-mentioned battery health estimation method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0180] Accordingly, this application provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the battery health state estimation method provided in the above embodiments.

[0181] This application provides an electronic device; Figure 10 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 10 As shown, the electronic device 1000 includes: a processor 1001, at least one communication bus 1002, a user interface 1003, at least one external communication interface 1004, and a memory 1005. The communication bus 1002 is configured to enable communication between these components. The user interface 1003 may include a display screen, and the external communication interface 1004 may include standard wired and wireless interfaces. The processor 1001 is configured to execute a program stored in the memory for estimating the battery health state, to implement the steps in the battery health state estimation method provided in the above embodiment.

[0182] The descriptions of the display device and storage medium embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0183] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0184] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0185] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0186] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0187] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0189] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0190] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0191] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for estimating the state of health of a battery, characterized in that, include: At the first moment of the battery standby time, the first current and the first terminal voltage of the battery are acquired, wherein the first current and the first terminal voltage are generated based on a reference DC pulse signal, and the standby time is the time during which the battery does not charge or discharge. A pulse test function is added to the battery converter. Based on the first current and the first terminal voltage, the test SOH of the battery is determined; At a second moment during the battery's operating time, the second current and the second terminal voltage of the battery are acquired, wherein the first moment and the second moment are within a preset time interval, the second current and the second terminal voltage are generated during the operation of the battery, and the battery operating time is the charging and discharging time of the battery; The estimated SOH of the battery is determined based on the second current, the second terminal voltage, and the prediction algorithm. If the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, the prediction algorithm parameters are adjusted to obtain the target prediction algorithm, and the SOH of the battery is estimated based on the target prediction algorithm. The method further includes: The first internal resistance is determined based on the first current and the first terminal voltage; Determine the target equivalent circuit model based on the first internal resistance; The internal resistance parameter of the battery is updated to the internal resistance parameter corresponding to the target equivalent circuit model, so that the latest internal resistance parameter is used for calculation when estimating the SOH of the battery in the next calculation.

2. The method according to claim 1, characterized in that, The method further includes: When the battery is detected to be in standby mode, a control signal is generated, which is used to control the battery to generate a reference DC pulse signal; The control signal is sent to the battery to obtain the first current and the first terminal voltage of the battery.

3. The method according to claim 1, characterized in that, The determination of the test battery health state (SOH) of the battery based on the first current and the first terminal voltage includes: Based on the first current and the first terminal voltage, the first internal resistance of the battery is determined; The test SOH of the battery is determined based on the first internal resistance.

4. The method according to claim 3, characterized in that, The test SOH of the battery based on the first internal resistance includes: Obtain the initial internal resistance and the end-of-life internal resistance of the battery; The first difference is determined based on the battery's initial internal resistance and the first internal resistance; The second difference is determined based on the initial internal resistance of the battery and the internal resistance at the end of the battery's lifespan. The test SOH of the battery is determined based on the first difference and the second difference.

5. The method according to claim 1, characterized in that, Based on the tested SOH and estimated SOH, determine whether the conditions for adjusting the prediction algorithm parameters are met, including: Determine the difference between the tested SOH and the estimated SOH; Determine the relationship between the difference and the preset threshold; Based on the size relationship, it is determined whether the conditions for adjusting the budget algorithm parameters are met. If it is determined that the conditions for adjusting the prediction algorithm parameters are not met, the prediction algorithm is determined as the target prediction algorithm.

6. A device for estimating the state of battery health, characterized in that, include: The first acquisition module is used to acquire the first current and the first terminal voltage of the battery at the first moment during the battery standby time. The first current and the first terminal voltage are generated based on a reference DC pulse signal. The standby time is the time during which the battery does not charge or discharge. A pulse test function is added to the battery converter. The first determining module is used to determine the test SOH of the battery based on the first current and the first terminal voltage; The second acquisition module is used to acquire the second current and the second terminal voltage of the battery at a second moment during the battery's operating time, wherein the first moment and the second moment are within a preset time interval, the second current and the second terminal voltage are generated during the operation of the battery, and the battery operating time is the charging and discharging time of the battery; The second determining module is used to determine the estimated SOH of the battery based on the second current, the second terminal voltage, and the prediction algorithm. An adjustment module is used to adjust the prediction algorithm parameters to obtain a target prediction algorithm when the conditions for adjusting the prediction algorithm parameters are met based on the tested SOH and the estimated SOH, so as to estimate the SOH of the battery based on the target prediction algorithm. The battery health status estimation device further includes: The fourth determining module is used to determine the first internal resistance based on the first current and the first terminal voltage; The fifth determining module is used to determine the target equivalent circuit model based on the first internal resistance; An update module is used to update the internal resistance parameter of the battery to the internal resistance parameter corresponding to the target equivalent circuit model, so that the latest internal resistance parameter is used for calculation when estimating the SOH of the battery in the next calculation.

7. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the battery health state estimation method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the battery health state estimation method as described in any one of claims 1 to 5.

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