Power battery SOC estimation method, system, vehicle and medium
Through dynamic selection correction method and Kalman filtering combined with equivalent circuit model to predict voltage, the error accumulation and insufficient accuracy of the existing SOC estimation algorithm are solved, and higher SOC estimation accuracy and robustness are achieved.
Patent Information
- Application Number
- CN202111663269.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-30
- Publication Date
- 2024-12-10
- Estimated Expiration
- 2041-12-30
AI Technical Summary
In the existing battery management system, the SOC estimation algorithm has problems such as accumulation of errors, not suitable for charging and discharging processes, and the accuracy depends on the identification of the equivalent circuit model parameters, resulting in insufficient SOC estimation accuracy.
By obtaining the current, voltage and temperature parameter information of the power battery, dynamically selecting the correction method or Kalman filtering method for SOC estimation, combining the equivalent circuit model to predict the voltage, correct the SOC value, and adapt to different working conditions and battery states.
It improves the accuracy and robustness of SOC estimation, is compatible with the impact of current magnification, SOC, temperature and battery aging on the predicted voltage, corrects the current sampling error and initial error, and enhances the adaptive ability of the algorithm.
Smart Images
Figure CN116413613B_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of battery management systems, and in particular to a SOC estimation method, system, vehicle and medium for a power battery. Background Art
[0002] The online estimation algorithm of SOC (State of charge) in the battery management system (BMS) is one of the core algorithms. As the core state quantity monitored by the BMS of new energy electric vehicles, SOC is the prerequisite for the accurate calculation of other battery state quantities, and its estimation accuracy is directly related to the performance of electric vehicles.
[0003] During the use of electric vehicles, the ambient temperature, operating conditions, battery aging status, and personal driving habits will affect the estimation accuracy of SOC. At present, the existing technology mainly estimates the battery SOC through the current ampere-hour integration method, the open circuit voltage method, or the KF filter algorithm correction method. However, when using these single algorithms to estimate the battery SOC, there are the following disadvantages:
[0004] (1) By integrating the current ampere-hour, SOC will be affected by current sampling error, capacity error, SOC initial error, etc., resulting in error accumulation. Therefore, in actual use, there will be many problems such as lack of correction ability and large errors.
[0005] (2) The open circuit voltage method requires the use of the battery terminal voltage after a long period of static state, and the OCV-SOC table obtained through offline battery testing to interpolate the current SOC of the battery. Therefore, it cannot be used for power batteries during the charging and discharging process.
[0006] (3) To estimate SOC using the KF filter algorithm correction method, it is usually necessary to first establish an equivalent circuit model of the battery and estimate its model parameters. Then, the model parameters and information such as current, voltage, and temperature are input into the KF filter observer to dynamically correct the SOC. The SOC estimation accuracy of this method is related to the identification accuracy of the equivalent circuit model parameters, so it is only applicable to working conditions where the current and voltage change dynamically. Summary of the invention
[0007] In order to overcome the problems existing in the related art, the present disclosure provides a SOC estimation method, system, vehicle and medium for a power battery.
[0008] According to a first aspect of an embodiment of the present disclosure, a method for estimating SOC of a power battery is provided, comprising:
[0009] Acquiring parameter information of the power battery, wherein the parameter information includes at least current, voltage and temperature;
[0010] When the parameter information is valid, judging whether the first correction method is satisfied based on the changes of the current, voltage and temperature;
[0011] If yes, then correcting the actual estimated value of the SOC of the power battery to a corresponding preset value by using the first correction method according to the parameter range of the parameter information;
[0012] If not, and when the changes in the current and voltage meet the preset dynamic change conditions, the true estimated value of the SOC of the power battery is obtained through the Kalman filtering method based on the voltage prediction value and the voltage, wherein the voltage prediction value is obtained based on the prediction of the equivalent circuit model.
[0013] In some embodiments, the current and voltage meet the preset dynamic change conditions when the following conditions are met, including:
[0014] Within a first preset time, the change amount of the current is not less than a first threshold, the power battery is in a non-charging state, and within the first preset time, the change direction of the current is opposite to the change direction of the voltage; or
[0015] When the last true estimated value of the SOC of the power battery is obtained by the Kalman filtering method, the parameter information is valid at the current moment, the power battery is in a non-charging state, and the duration for which the change in current is less than the second threshold is less than the second preset time.
[0016] Furthermore, the obtaining of the true estimated value of the SOC of the power battery by a Kalman filter method based on the voltage prediction value and the voltage includes:
[0017] Based on the parameter information, obtaining model parameters of an equivalent circuit model of the power battery;
[0018] The ampere-hour integration method is used to obtain the prior SOC value of the filter observer;
[0019] Based on the model parameters of the equivalent circuit model, obtaining a voltage prediction value estimated by the equivalent circuit model;
[0020] Based on the voltage prediction value and the voltage, obtaining an SOC correction coefficient through the filter observer;
[0021] The priori SOC value is corrected based on the SOC correction coefficient to obtain a true estimated value of the SOC of the power battery.
[0022] In some embodiments, if yes, then according to the parameter range of the parameter information, the actual estimated value of the SOC of the power battery is corrected to a corresponding preset value by the first correction method, including:
[0023] When the following conditions are met within the first preset time, the actual estimated value of the SOC is corrected to a first preset value:
[0024] The absolute value of the current is not greater than a third threshold, the voltage is not less than an upper cut-off voltage, and the temperature is not less than a fourth threshold.
[0025] Furthermore, it also includes:
[0026] When the following conditions are met within the first preset time, the actual estimated value of the SOC is corrected to a second preset value:
[0027] The absolute value of the current is not greater than a third threshold, the voltage is not greater than a lower cut-off voltage, and the temperature is not less than a fourth threshold.
[0028] Furthermore, it also includes:
[0029] When the following conditions are met, the true estimated value of SOC is obtained based on the open circuit voltage method:
[0030] The absolute value of the current is not greater than the fifth threshold, and the duration for which the absolute value of the current is not greater than the fifth threshold is not less than a preset duration at the weighted temperature of the power battery.
[0031] In some embodiments, it also includes:
[0032] When the parameter information is valid and does not satisfy the first correction method and the Kalman filter method, the ampere-hour integration method is applied to obtain the true estimated value of the SOC; or
[0033] When the voltage and temperature are abnormal and the current is valid, the ampere-hour integration method is applied to obtain the true estimated value of the SOC.
[0034] Further, when the voltage and temperature are abnormal and the current is valid, the real estimated value of the SOC is obtained by applying the ampere-hour integration method, which also includes:
[0035] If the current is abnormal, the most recent true estimated value of the SOC of the power battery is used as the true estimated value of the SOC.
[0036] In some embodiments, after obtaining the true estimated value of the SOC of the power battery, the method further includes:
[0037] Based on a preset SOC update rate factor, establishing a corresponding relationship between the SOC output variable and a true estimated value of the SOC of the power battery;
[0038] The SOC output variable is displayed.
[0039] According to a second aspect of an embodiment of the present disclosure, a SOC estimation system for a power battery is provided, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the SOC estimation method for the power battery as described in the first aspect.
[0040] According to a third aspect of an embodiment of the present disclosure, a vehicle is provided, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the SOC estimation method of the power battery as described in the first aspect.
[0041] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided. When instructions in the non-temporary computer-readable storage medium are executed by a processor of a vehicle, the vehicle is enabled to execute the SOC estimation method for the power battery described in any one of the first aspects.
[0042] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided. When instructions in the computer program product are executed by a processor of a vehicle, the vehicle is enabled to execute the SOC estimation method of the power battery according to any one of the first aspects.
[0043] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: the present application selects different estimation methods according to the dynamic change conditions of the parameter information of the power battery. When the current, voltage and temperature meet the first correction method, the real estimated value of SOC can be corrected by the estimation methods of other algorithms, making full use of the advantages of each estimation algorithm and improving the accuracy of SOC estimation. When the current, voltage and temperature do not meet the first correction method, and the current and voltage meet the preset dynamic change conditions, the real estimated value of SOC of the power battery is obtained by the Kalman filter method, and the predicted value of the power battery voltage is predicted by the equivalent circuit model, which can be compatible with the influence of current rate, SOC, temperature and battery aging on the accuracy of predicted voltage. The voltage value of the battery can be accurately estimated throughout its life cycle, and the SOC error caused by factors such as current sampling error, SOC initial error, and capacity error can also be corrected, thereby improving the robustness and adaptability of the algorithm.
[0044] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0046] Figure 1 The figure is a flow chart of a method for estimating the SOC of a power battery according to an exemplary embodiment.
[0047] Figure 2 It is a schematic diagram of the vehicle structure of a SOC estimation system for a power battery suitable for implementing an embodiment of the present application, according to an exemplary embodiment. DETAILED DESCRIPTION
[0048] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.
[0049] Figure 1 is a flow chart of a method for estimating the SOC of a power battery according to an exemplary embodiment. Figure 1 As shown, the following steps are included:
[0050] In step S101, parameter information of a power battery is acquired, where the parameter information at least includes current, voltage and temperature.
[0051] Specifically, the parameter information of the power battery can be obtained through the monitoring chip or sensor installed on the power battery. The parameter information includes values such as current, voltage and temperature, and the parameter information of the power battery is monitored in real time.
[0052] In step S102, when the parameter information is valid, it is determined whether the first correction method is satisfied based on the changes in the current, voltage and temperature.
[0053] Specifically, the current, voltage and temperature of the power battery have a certain valid value range, for example, the valid value of voltage is (0mV~5000mV), the valid value of temperature is (-40℃~125℃), and the valid value of current is (-1000A~1000A). Within this value range, it is valid. When the parameter information is valid, different estimation methods are executed by judging whether the current, voltage and temperature meet the first correction method.
[0054] In step S103, if yes, then the actual estimated value of the SOC of the power battery is corrected to a corresponding preset value by the first correction method according to the parameter range of the parameter information;
[0055] Specifically, when the current, voltage and temperature meet the first correction method, since the first correction method includes different estimation methods, including full charge correction, full discharge correction and static voltage correction, according to the parameter range of the parameter information, an estimation method that meets the conditions is obtained to correct the actual estimated value of the SOC of the power battery to the preset value corresponding to the different estimation methods.
[0056] In some embodiments, if yes, then according to the parameter range of the parameter information, the actual estimated value of the SOC of the power battery is corrected to a corresponding preset value by the first correction method, including:
[0057] When the following conditions are met within the first preset time, the actual estimated value of the SOC is corrected to a first preset value:
[0058] The absolute value of the current is not greater than a third threshold, the voltage is not less than an upper cut-off voltage, and the temperature is not less than a fourth threshold.
[0059] Specifically, the first preset time is two adjacent moments, that is, the time when the SOC algorithm is called twice in succession. When the first correction method is met, taking the third threshold value of 0.2C and the fourth threshold value of 10℃ as an example, that is, within the time when the SOC algorithm is called twice in succession, when the absolute value of the current is not greater than 0.2C, the voltage is not less than the upper cut-off voltage of the power battery, and the power battery temperature is not less than 10℃, then the full charge correction method is adopted at this time, and the real estimated value of SOC is directly corrected to 100%, which is the first preset value.
[0060] In some embodiments, it also includes:
[0061] When the following conditions are met within the first preset time, the actual estimated value of the SOC is corrected to a second preset value:
[0062] The absolute value of the current is not greater than a third threshold, the voltage is not greater than a lower cut-off voltage, and the temperature is not less than a fourth threshold.
[0063] Specifically, when the first correction method is met, taking the third threshold value of 0.2C and the fourth threshold value of 10°C as an example, within the first preset time, that is, within the time of calling the SOC algorithm calculation twice in a row, when the absolute value of the current is not greater than 0.2C, the voltage is not greater than the lower cut-off voltage of the power battery, and the power battery temperature is not less than 10°C, then the full discharge correction method is adopted at this time, and the actual estimated value of the SOC is directly corrected to 0, which is the second preset value.
[0064] In some embodiments, it also includes:
[0065] When the following conditions are met, the true estimated value of SOC is obtained based on the open circuit voltage method:
[0066] The absolute value of the current is not greater than the fifth threshold, and the duration for which the absolute value of the current is not greater than the fifth threshold is not less than a preset duration at the weighted temperature of the power battery.
[0067] Specifically, taking the fifth threshold of 1A as an example, that is to say, when the first correction method is satisfied, the absolute value of the current is not greater than 1A, and the duration of the absolute value of the current not greater than 1A is not less than the preset duration at the weighted temperature of the power battery, then the correction method of static voltage correction is used, that is, the OCV-SOC data obtained through the battery offline test is obtained by linear interpolation to obtain the corresponding SOC true estimation value. The weighted temperature specifically refers to: the initial value of the weighted temperature when the electric vehicle is just powered on defaults to -30°C, and the preset duration corresponding to the weighted temperature is 2h; thereafter, Tweighted (k) = Tweighted (k-1) + θ1*Tactual (k), where k and k-1 represent two adjacent moments, and θ1 represents the weighting coefficient. The lower the weighted temperature, the longer the preset duration.
[0068] In step S104, if no, and when the changes in the current and voltage meet the preset dynamic change conditions, the true estimated value of the SOC of the power battery is obtained through the Kalman filtering method based on the voltage prediction value and the voltage, wherein the voltage prediction value is obtained based on the prediction of the equivalent circuit model.
[0069] Specifically, when the first correction method is not met by the change of current, voltage and temperature, the change of current and voltage is detected to see whether it meets the preset dynamic change conditions. If so, the real estimated value of the SOC of the power battery is obtained by the Kalman filter method through the voltage prediction value and the voltage. When the battery is corrected by the Kalman filter method, the Kalman filter method will be triggered for correction. Only when (1) the charge and discharge mode is converted to charging, (2) the cumulative time of the absolute value of the current change at adjacent moments is less than 5A is not less than 5 minutes, (3) the current, voltage, and temperature are invalid, or any of the above conditions are met, the Kalman filter method correction is turned off.
[0070] In some embodiments, the current and voltage meet the preset dynamic change conditions when the following conditions are met, including:
[0071] Within a first preset time, the change amount of the current is not less than a first threshold, the power battery is in a non-charging state, and within the first preset time, the change direction of the current is opposite to the change direction of the voltage; or
[0072] When the last true estimated value of the SOC of the power battery is obtained by the Kalman filtering method, the parameter information is valid at the current moment, the power battery is in a non-charging state, and the duration for which the change in current is less than the second threshold is less than the second preset time.
[0073] Specifically, the first preset time is two adjacent moments, that is, the time when the SOC algorithm is called twice in succession. Take the first threshold value as 0.1C, the second threshold value as 5A, and the second preset time as 5min as an example, that is to say, within the time when the SOC algorithm is called twice in succession, the change of the current is not less than 0.1C, and the current power battery is in a non-charging state, and within the first preset time, the change direction of the current is opposite to the change direction of the voltage, for example, the discharge current increases, while the discharge voltage decreases, then it is judged that the current and voltage meet the preset dynamic change conditions. Alternatively, when the last SOC true estimation value of the power battery is obtained by the Kalman filter method, and the parameter information at the current moment is valid, and the power battery is in a non-charging state and the duration of the change of the current is less than 5A is less than 5min, then it can also be judged that the current and voltage meet the preset dynamic change conditions.
[0074] In some embodiments, the obtaining of the true estimated value of the SOC of the power battery by a Kalman filter method based on the voltage prediction value and the voltage includes:
[0075] Based on the parameter information, obtaining model parameters of an equivalent circuit model of the power battery;
[0076] The ampere-hour integration method is used to obtain the prior SOC value of the filter observer;
[0077] Based on the model parameters of the equivalent circuit model, obtaining a voltage prediction value estimated by the equivalent circuit model;
[0078] Based on the voltage prediction value and the voltage, obtaining an SOC correction coefficient through the filter observer;
[0079] The priori SOC value is corrected based on the SOC correction coefficient to obtain a true estimated value of the SOC of the power battery.
[0080] Specifically, when the current and voltage meet the preset dynamic change conditions, it is determined that the dynamic voltage correction method is used at this time, that is, the model parameters of the equivalent circuit model are obtained through the power battery parameter information obtained, and the obtained model parameters are used as input. The voltage prediction value estimated by the equivalent circuit model is obtained through the equivalent circuit model, and the SOC correction coefficient is obtained through the voltage prediction value estimated by the equivalent circuit model and the current voltage of the power battery. At the same time, the priori SOC value of the filter observer is obtained through the ampere-hour integration method, and the priori SOC value is corrected by the SOC correction coefficient to obtain the true estimated SOC value of the power battery.
[0081] In some embodiments, it also includes:
[0082] When the parameter information is valid and does not satisfy the first correction method and the Kalman filter method, the ampere-hour integration method is applied to obtain the true estimated value of the SOC; or
[0083] When the voltage and temperature are abnormal and the current is valid, the ampere-hour integration method is applied to obtain the true estimated value of the SOC.
[0084] Specifically, when the parameter information is valid but does not meet the first correction method and the Kalman filter method, the ampere-hour integration method is applied to obtain the true estimated value of the SOC. Alternatively, when the voltage and temperature are abnormal and the current is valid, the ampere-hour integration method is also applied to obtain the true estimated value of the SOC. In other words, the ampere-hour integration method is used as the final bottom-line correction method.
[0085] In some embodiments, when the voltage and temperature are abnormal and the current is valid, the real estimated value of the SOC is obtained by applying the ampere-hour integration method, further comprising:
[0086] If the current is abnormal, the most recent true estimated value of the SOC of the power battery is used as the true estimated value of the SOC.
[0087] Specifically, if the current is abnormal, the most recent true SOC estimation value of the power battery is used as the true SOC estimation value, that is, the true SOC estimation value estimated before the current is abnormal is used as the corrected true SOC estimation value.
[0088] In some embodiments, after obtaining the true estimated value of the SOC of the power battery, the method further includes:
[0089] Based on a preset SOC update rate factor, establishing a corresponding relationship between the SOC output variable and a true estimated value of the SOC of the power battery;
[0090] The SOC output variable is displayed.
[0091] Specifically, a corresponding relationship between the SOC output variable and the actual estimated value of the SOC of the power battery is established through a preset SOC update rate factor, as follows:
[0092] SOC_out(k)=SOC_out(k-1)+Speed(k)*delta_SOC_act(k);
[0093] Speed(k)=φ1*delta_SOC_diff(k)+φ2*delta_SOC_act(k)+φ3*delta_SOC_thre+φ4*Tmeasured(k);
[0094] delta_SOC_diff(k)=SOC_real(k)-SOC_out(k-1);
[0095] Among them, k and k-1 represent two adjacent moments;
[0096] SOC_out represents the SOC output variable;
[0097] SOC_real represents the real estimated value of SOC;
[0098] Speed represents the SOC update rate factor;
[0099] delta_SOC_act represents the actual SOC change between two adjacent moments;
[0100] delta_SOC_diff represents the difference between the current SOC_real and the previous SOC_out;
[0101] delta_SOC_thre represents the maximum change threshold of SOC between two adjacent moments;
[0102] Tactual is the actual measured temperature;
[0103] φ1, φ2, φ3, and φ4 represent correlation coefficients.
[0104] After calculating the SOC output variable, the SOC output variable is displayed on the display screen. The preset SOC update rate factor can ensure that SOC_out is continuously corrected while preventing its sudden change from affecting the entire BMS control strategy.
[0105] The present application also discloses a SOC estimation system for a power battery, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded by the processor and executes the SOC estimation method for the power battery in the above embodiment.
[0106] Regarding the system in the above embodiment, the specific manner of performing the operations therein has been described in detail in the embodiment of the method, and will not be elaborated here.
[0107] Reference below Figure 2 , the present application provides a vehicle. Figure 2 A schematic diagram of a vehicle structure suitable for implementing a SOC estimation system for a power battery of an embodiment of the present application is shown. The system includes a processor and a memory, the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the following steps: obtaining parameter information of a power battery, the parameter information including at least current, voltage and temperature; if the parameter information is valid, judging whether a first correction method is satisfied based on the change of the current, voltage and temperature; if yes, correcting the SOC true estimation value of the power battery to a corresponding preset value through the first correction method according to the parameter range of the parameter information; if no, and when the change of the current and voltage meets the preset dynamic change condition, obtaining the SOC true estimation value of the power battery through the Kalman filter method based on the voltage prediction value and the voltage, wherein the voltage prediction value is obtained based on the prediction of the equivalent circuit model.
[0108] In the embodiment of the present application, the processor is a processing device that performs logical operations, such as a central processing unit (CPU), a field programmable logic array (FPGA), a digital signal processor (DSP), a single-chip microcomputer (MCU), an application-specific logic circuit (ASIC), an image processing unit (GPU), etc., which have data processing capabilities and / or program execution capabilities. It is easy to understand that the processor is usually connected to the memory in communication, and any combination of one or more computer program products is stored on the memory. The memory may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), USB memory, flash memory, etc. One or more computer instructions may be stored on the memory, and the processor may run the computer instructions to implement related analysis functions. Various applications and various data, such as various data used and / or generated by the application, may also be stored in the computer-readable storage medium.
[0109] In the embodiments of the present application, each module can run on the same processor or on multiple processors; each module can run on a processor of the same architecture, for example, all running on a processor of the X86 system, or can run on processors of different architectures, for example, the image processing module runs on the CPU of the X86 system, and the machine learning module runs on the GPU. Each module can be encapsulated in a computer product, for example, each module is encapsulated in a computer software and runs on a computer (server), or each or part of it can be encapsulated in different computer products, for example, the image processing module is encapsulated in a computer software and runs on a computer (server), and the machine learning modules are respectively encapsulated in separate computer software and run on another or more computers (servers); the computing platform when each module is executed can be local computing, cloud computing, or a hybrid computing consisting of local computing and cloud computing.
[0110] like Figure 2 As shown, the computer system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation instructions of the system are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.
[0111] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.
[0112] The present application also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal can execute to implement the following steps: obtaining parameter information of the power battery, the parameter information including at least current, voltage and temperature; when the parameter information is valid, judging whether a first correction method is satisfied based on the changes in the current, voltage and temperature; if yes, then correcting the SOC true estimate value of the power battery to a corresponding preset value through the first correction method according to the parameter range of the parameter information; if not, and when the changes in the current and voltage meet the preset dynamic change conditions, then obtaining the SOC true estimate value of the power battery through the Kalman filtering method based on the voltage prediction value and the voltage, wherein the voltage prediction value is obtained based on the prediction of the equivalent circuit model.
[0113] In one embodiment, a computer program product is provided. When the instructions in the computer program product are executed by a processor of a mobile terminal, the mobile terminal is enabled to perform the following steps: obtaining parameter information of a power battery, the parameter information including at least current, voltage and temperature; when the parameter information is valid, judging whether a first correction method is satisfied based on changes in the current, voltage and temperature; if so, correcting a true estimated value of the SOC of the power battery to a corresponding preset value through the first correction method according to a parameter range of the parameter information; if not, and when changes in the current and voltage satisfy a preset dynamic change condition, obtaining a true estimated value of the SOC of the power battery through a Kalman filtering method based on a voltage prediction value and the voltage, wherein the voltage prediction value is obtained based on prediction of an equivalent circuit model.
[0114] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium such as a computer-readable storage medium, which may send, propagate or transmit a program for use by or in combination with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above. Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art that are not disclosed in this disclosure. The specification and embodiments are intended to be exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.
[0115] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
Claims
1. A method for estimating the SOC of a power battery, characterized in that: include: Acquiring parameter information of the power battery, wherein the parameter information includes at least current, voltage and temperature; When the parameter information is valid, judging whether the first correction method is satisfied based on the changes of the current, voltage and temperature; If yes, then correcting the actual estimated value of the SOC of the power battery to a corresponding preset value by using the first correction method according to the parameter range of the parameter information; If not, and when the changes in the current and voltage meet the preset dynamic change conditions, a true estimated value of the SOC of the power battery is obtained by a Kalman filter method based on a voltage prediction value and the voltage, wherein the voltage prediction value is obtained based on an equivalent circuit model prediction; If yes, then according to the parameter range of the parameter information, the actual estimated value of the SOC of the power battery is corrected to a corresponding preset value by the first correction method, including: When the following conditions are met within the first preset time, the actual estimated value of the SOC is corrected to a first preset value: The absolute value of the current is not greater than a third threshold, the voltage is not less than an upper cut-off voltage, and the temperature is not less than a fourth threshold, and the first preset time is the time between two consecutive calls to the SOC estimation method; If not, and when the changes in the current and voltage meet the preset dynamic change conditions, the method includes: Within a first preset time, the change amount of the current is not less than a first threshold, the power battery is in a non-charging state, and within the first preset time, the change direction of the current is opposite to the change direction of the voltage; or When the last true estimated value of the SOC of the power battery is obtained by the Kalman filtering method, the parameter information is valid at the current moment, the power battery is in a non-charging state, and the duration for which the change in current is less than the second threshold is less than the second preset time.
2. The SOC estimation method of a power battery according to claim 1, characterized in that: The obtaining the true estimated value of the SOC of the power battery by a Kalman filter method based on the voltage prediction value and the voltage includes: Based on the parameter information, obtaining model parameters of an equivalent circuit model of the power battery; The ampere-hour integration method is used to obtain the prior SOC value of the filter observer; Based on the model parameters of the equivalent circuit model, obtaining a voltage prediction value estimated by the equivalent circuit model; Based on the voltage prediction value and the voltage, obtaining an SOC correction coefficient through the filter observer; The priori SOC value is corrected based on the SOC correction coefficient to obtain a true estimated value of the SOC of the power battery.
3. The SOC estimation method of a power battery according to claim 1, characterized in that: Also includes: When the following conditions are met within the first preset time, the actual estimated value of the SOC is corrected to a second preset value: The absolute value of the current is not greater than a third threshold, the voltage is not greater than a lower cut-off voltage, and the temperature is not less than a fourth threshold.
4. The SOC estimation method of a power battery according to claim 1, characterized in that: Also includes: When the following conditions are met, the true estimated value of SOC is obtained based on the open circuit voltage method: The absolute value of the current is not greater than the fifth threshold, and the duration for which the absolute value of the current is not greater than the fifth threshold is not less than a preset duration at the weighted temperature of the power battery.
5. The SOC estimation method of a power battery according to claim 1, characterized in that: Also includes: When the parameter information is valid and does not satisfy the first correction method and the Kalman filter method, the ampere-hour integration method is applied to obtain the true estimated value of the SOC; or When the voltage and temperature are abnormal and the current is valid, the ampere-hour integration method is applied to obtain the true estimated value of the SOC.
6. The SOC estimation method of a power battery according to claim 5, characterized in that: When the voltage and temperature are abnormal and the current is valid, the ampere-hour integration method is applied to obtain the true estimated value of the SOC, further comprising: If the current is abnormal, the most recent true estimated value of the SOC of the power battery is used as the true estimated value of the SOC.
7. The SOC estimation method of a power battery according to claim 1, characterized in that: After obtaining the real estimated value of the SOC of the power battery, the method further includes: Based on a preset SOC update rate factor, establishing a corresponding relationship between the SOC output variable and a true estimated value of the SOC of the power battery; The SOC output variable is displayed.
8. A power battery SOC estimation system, characterized in that: include: A processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the SOC estimation method of the power battery according to any one of claims 1-7.
9. A vehicle, characterized in that: include: A processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the SOC estimation method of the power battery according to any one of claims 1-7.
10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the non-transitory computer-readable storage medium are executed by a processor of a vehicle, the vehicle is enabled to execute the SOC estimation method for a power battery according to any one of claims 1-7.