CN116413613A - SOC estimation method, system, vehicle and medium of power battery - Google Patents
SOC estimation method, system, vehicle and medium of power battery Download PDFInfo
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Abstract
The disclosure relates to a method, a system, a vehicle and a medium for estimating the SOC of a power battery, comprising: acquiring parameter information of the power battery, wherein the parameter information at least comprises current, voltage and temperature; judging whether the first correction mode is met or not based on the change conditions of current, voltage and temperature under the condition that the parameter information is effective; if so, correcting the real SOC estimation value of the power battery to a corresponding preset value in a first correction mode according to the parameter range of the parameter information; if not, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery through a Kalman filtering method based on the voltage predicted value and the voltage, wherein the voltage predicted value is obtained based on the equivalent circuit model prediction. According to the method and the device, the real estimated value of the SOC is corrected and obtained through the dynamic switching of the proper correction algorithm of the parameter information of the power battery, so that the accuracy and the stability of the real estimated value of the SOC of the power battery are improved.
Description
Technical Field
The disclosure relates to the technical field of battery management systems, and in particular relates to an SOC estimation method, system, vehicle and medium of a power battery.
Background
An online estimation algorithm of SOC (State of charge) in a battery management system (battery management system, BMS) is one of the core algorithms. As a core state quantity monitored by the BMS of the new energy electric automobile, the SOC is a precondition for realizing the accurate calculation of other battery state quantities, and the estimation accuracy is directly related to the performance of the electric automobile.
In the use process of the electric automobile, the estimation accuracy of the SOC can be influenced by the ambient temperature, the operation condition, the battery aging state and the individual driving habit. Currently, in the prior art, the SOC of the battery is estimated mainly by a current ampere-hour integration method, an open circuit voltage method or a KF filtering algorithm correction method. The following drawbacks exist when using these singular algorithms to estimate the battery SOC:
(1) By integrating the current ampere-hour, the SOC is affected by current sampling errors, capacity errors, initial errors of the SOC, and the like, resulting in error accumulation. Therefore, there are many problems in actual use, such as the lack of correction capability and the large error.
(2) The open circuit voltage method needs to utilize the battery terminal voltage after long-time standing, and interpolates the current SOC of the battery through an OCV-SOC table obtained by offline battery test, so that the open circuit voltage method cannot be used for a power battery in the charging and discharging process.
(3) The SOC is estimated by using a correction method of KF filtering algorithm, and usually, an equivalent circuit model of the battery is first established and model parameters thereof are estimated. And then inputting information such as model parameters, current, voltage, temperature and the like to a KF type filtering observer to dynamically correct the SOC. The SOC estimation precision of the mode is related to the identification precision of the equivalent circuit model parameters, so that the method is only suitable for the working conditions of current and voltage dynamic changes.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a SOC estimation method, system, vehicle, and medium for a power battery.
According to a first aspect of an embodiment of the present disclosure, there is provided a SOC estimation method of a power battery, including:
acquiring parameter information of a power battery, wherein the parameter information at least comprises current, voltage and temperature;
judging whether a first correction mode is met or not based on the change conditions of the current, the voltage and the temperature under the condition that the parameter information is effective;
if so, correcting the real SOC estimation value of the power battery to a corresponding preset value in the first correction mode according to the parameter range of the parameter information;
if not, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery through a Kalman filtering method based on a voltage predicted value and the voltage, wherein the voltage predicted value is predicted based on an equivalent circuit model.
In some embodiments, the current and voltage satisfy a preset dynamically changing condition when the following condition is satisfied, comprising:
the change amount of the current is not smaller than a first threshold value in a first preset time, the power battery is in a non-charging state, and the change direction of the current is opposite to the change direction of the voltage in the first preset time; or,
and under the condition that the real SOC estimated value of the last power battery is obtained by a Kalman filtering method, the parameter information is valid at the current moment, the power battery is in a non-charging state, and the duration that the variation of the current is smaller than a second threshold value is smaller than a second preset time.
Further, the obtaining, based on the voltage predicted value and the voltage, the SOC real estimated value of the power battery by a kalman filtering method includes:
based on the parameter information, obtaining model parameters of an equivalent circuit model of the power battery;
applying an ampere-hour integration method to obtain a priori SOC value of the filtering observer;
obtaining a voltage predicted value estimated by the equivalent circuit model based on model parameters of the equivalent circuit model;
based on the voltage predicted value and the voltage, obtaining an SOC correction coefficient through the filtering observer;
and correcting the priori SOC value based on the SOC correction coefficient to obtain the true SOC estimated value of the power battery.
In some embodiments, if yes, correcting, according to the parameter range of the parameter information, the SOC real estimation value of the power battery to a corresponding preset value in the first correction manner, including:
correcting the SOC real estimation value to a first preset value when the following conditions are met in the first preset time:
the absolute value of the current is not greater than a third threshold, the voltage is not less than an upper cutoff voltage, and the temperature is not less than a fourth threshold.
Further, the method further comprises the following steps:
correcting the SOC real estimation value to a second preset value when the following conditions are met within the first preset time:
the absolute value of the current is not greater than a third threshold, the voltage is not greater than a lower cutoff voltage, and the temperature is not less than a fourth threshold.
Further, the method further comprises the following steps:
obtaining the SOC real estimation value based on an open circuit voltage method when the following conditions are satisfied:
the absolute value of the current is not greater than a fifth threshold value, and the duration of the absolute value of the current not greater than the fifth threshold value is not less than a preset duration at the weighted temperature of the power battery.
In some embodiments, further comprising:
when the parameter information is effective and the first correction mode and the Kalman filtering method are not satisfied, an ampere-hour integration method is applied to obtain the SOC real estimated value; or alternatively
And when the voltage and the temperature are abnormal and the current is effective, obtaining the SOC real estimated value by applying the ampere-hour integration method.
Further, when the voltage and the temperature are abnormal and the current is effective, an ampere-hour integration method is applied to obtain the true estimated value of the SOC, and the method further comprises the steps of:
and if the current is abnormal, taking the SOC real estimated value of the last power battery as the SOC real estimated value.
In some embodiments, after obtaining the SOC real estimation value of the power battery, the method further includes:
based on a preset SOC update rate factor, establishing a corresponding relation between an SOC output variable and an SOC true estimated value of the power battery;
and displaying the SOC output variable.
According to a second aspect of embodiments of the present disclosure, there is provided an SOC estimation system for a power battery, including: a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, 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 the first aspect.
According to a third aspect of embodiments of the present disclosure, there is provided a vehicle comprising: a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, 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 the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, which when executed by a processor of a vehicle, enables the vehicle to perform the SOC estimation method of the power battery of any one of the first aspects.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product, which when executed by a processor of a vehicle, enables the vehicle to perform the SOC estimation method of a power battery according to any of the first aspects.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: according to the method, different estimation methods are selected according to the dynamic change conditions of the parameter information of the power battery, and when the current, the voltage and the temperature meet the first correction mode, the real estimation value of the SOC can be corrected through the estimation modes of other algorithms, so that the advantages of the estimation algorithms are fully utilized, and the accuracy of SOC estimation is improved. When the current, the voltage and the temperature do not meet the first correction mode and the current and the voltage meet the preset dynamic change conditions, the real SOC estimated value of the power battery is obtained through a Kalman filtering method, the predicted voltage value of the power battery is predicted through an equivalent circuit model, and the influences of current multiplying power, SOC, temperature and battery aging on the precision of the predicted voltage can be compatible. The voltage value of the battery can be accurately estimated in the whole life cycle of the battery, and the SOC error caused by factors such as current sampling error, SOC initial error, capacity error and the like can be corrected, so that the robustness and the self-adaption capability of the algorithm are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
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.
Fig. 1 is a flowchart illustrating a SOC estimation method of a power battery according to an exemplary embodiment.
Fig. 2 is a schematic diagram showing a vehicle structure of an SOC estimation system suitable for use in implementing the power battery of the embodiment of the present application, according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Fig. 1 is a flowchart illustrating a SOC estimation method of a power battery according to an exemplary embodiment, as shown in fig. 1, including the steps of:
in step S101, parameter information of the power battery is acquired, the parameter information including at least current, voltage, and temperature.
Specifically, through the monitoring chip or the sensor of installing on power battery, can acquire power battery's parameter information, parameter information includes numerical value such as electric current, voltage and temperature, and power battery's parameter information is real-time supervision.
In step S102, if the parameter information is valid, it is determined whether or not the first correction method is satisfied based on the change conditions of the current, the voltage, and the temperature.
Specifically, the current, voltage and temperature of the power battery have a certain effective value range, for example, the effective value of the voltage is (0 mV-5000 mV), the effective value of the temperature is (-40-125 ℃), the effective value of the current is (-1000A), and the power battery is effective within the effective value range. When the parameter information is valid, different estimation methods are performed by judging whether the current, the voltage and the temperature satisfy the first correction method.
In step S103, if yes, correcting the SOC real estimation value of the power battery to a corresponding preset value by the first correction method according to the parameter range of the parameter information;
specifically, when the current, the voltage and the temperature satisfy the first correction mode, the first correction mode includes different estimation methods, and specifically includes full charge correction, full discharge correction and static voltage correction. And acquiring an estimation method meeting the conditions according to the parameter range of the parameter information to correct the real SOC estimation value of the power battery to a preset value corresponding to different estimation methods.
In some embodiments, if yes, correcting, according to the parameter range of the parameter information, the SOC real estimation value of the power battery to a corresponding preset value in the first correction manner, including:
correcting the SOC real estimation value to a first preset value when the following conditions are met in the first preset time:
the absolute value of the current is not greater than a third threshold, the voltage is not less than an upper cutoff voltage, and the temperature is not less than a fourth threshold.
Specifically, the first preset time is two adjacent moments, namely, the time calculated by calling the SOC algorithm twice in succession. When the first correction mode is met, taking the third threshold value as 0.2C and the fourth threshold value as 10 ℃ as an example, that is, when the absolute value of current is not more than 0.2C and the voltage is not less than the upper cut-off voltage of the power battery and the temperature of the power battery is not less than 10 ℃ in the time of continuously calling the calculation of the SOC algorithm, the full charge correction mode is adopted at the moment, and the real estimated value of the SOC is directly corrected to 100%, namely the first preset value.
In some embodiments, further comprising:
correcting the SOC real estimation value to a second preset value when the following conditions are met within the first preset time:
the absolute value of the current is not greater than a third threshold, the voltage is not greater than a lower cutoff voltage, and the temperature is not less than a fourth threshold.
Specifically, when the first correction mode is satisfied, taking the third threshold value as 0.2C and the fourth threshold value as 10 ℃ as an example, in the first preset time, that is, in the time of continuously calling the calculation of the SOC algorithm twice, when the absolute value of the current is not more than 0.2C, the voltage is not more than the lower cut-off voltage of the power battery, and the temperature of the power battery is not less than 10 ℃, then adopting the full-discharge correction mode to directly correct the true estimated value of the SOC to 0, that is, the second preset value.
In some embodiments, further comprising:
obtaining the SOC real estimation value based on an open circuit voltage method when the following conditions are satisfied:
the absolute value of the current is not greater than a fifth threshold value, and the duration of the absolute value of the current not greater than the fifth threshold value is not less than a preset duration at the weighted temperature of the power battery.
Specifically, taking the fifth threshold value as 1A as an example, that is, when the first correction mode is satisfied, the absolute value of the current is not greater than 1A, and the duration of the absolute value of the current is not greater than 1A and is not less than the preset duration at the weighted temperature of the power battery, the correction mode of static voltage correction, that is, OCV-SOC data obtained through offline battery test, is used to obtain the corresponding SOC real estimated value through a linear interpolation mode. Wherein the weighted temperature specifically refers to: the initial value of the weighted temperature of the electric automobile when the electric automobile is just electrified defaults to minus 30 ℃, and the preset time length corresponding to the weighted temperature is 2h; thereafter T-weighting (k) =t-weighting (k-1) +θ 1*T, where k and k-1 represent the adjacent two instants and θ1 represents the weighting factor. The lower the weighted temperature, the longer the preset duration.
In step S104, if not, and when the current and voltage change conditions meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery by a kalman filtering method based on a voltage predicted value and the voltage, wherein the voltage predicted value is predicted based on an equivalent circuit model.
Specifically, when the first correction mode is not met through the change conditions of the current, the voltage and the temperature, whether the change conditions of the current and the voltage meet the preset dynamic change conditions is detected, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, the real SOC estimation value of the power battery is obtained through a Kalman filtering method through the voltage prediction value and the voltage. When the battery is corrected by adopting the Kalman filtering method, the Kalman filtering method is triggered for correction all the time, and the Kalman filtering method is closed for correction only when (1) the charge and discharge mode is converted into charge, (2) the accumulated time length of current change absolute value at adjacent time is smaller than 5A is not smaller than 5min, and (3) the current, voltage and temperature are invalid and any one of the conditions is met.
In some embodiments, the current and voltage satisfy a preset dynamically changing condition when the following condition is satisfied, comprising:
the change amount of the current is not smaller than a first threshold value in a first preset time, the power battery is in a non-charging state, and the change direction of the current is opposite to the change direction of the voltage in the first preset time; or,
and under the condition that the real SOC estimated value of the last power battery is obtained by a Kalman filtering method, the parameter information is valid at the current moment, the power battery is in a non-charging state, and the duration that the variation of the current is smaller than a second threshold value is smaller than a second preset time.
Specifically, the first preset time is two adjacent moments, namely, the time calculated by calling the SOC algorithm twice in succession. Taking 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, the current change amount is not less than 0.1C in the time of continuously calling the calculation of the SOC algorithm, and the current power battery is in a non-charging state, and in the first preset time, the current change direction is opposite to the voltage change direction, for example, the discharge current is increased, and the discharge voltage is reduced, so that the current and the voltage are judged to meet the preset dynamic change condition. Or when the SOC real estimated value of the last power battery is obtained by the kalman filtering method, and the current time parameter information is valid, and the power battery is in a non-charging state and the duration of the current change less than 5A is less than 5min, it may also be determined that the current and the voltage meet the preset dynamic change condition.
In some embodiments, the obtaining the SOC real estimation value of the power battery by a kalman filtering 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;
applying an ampere-hour integration method to obtain a priori SOC value of the filtering observer;
obtaining a voltage predicted value estimated by the equivalent circuit model based on model parameters of the equivalent circuit model;
based on the voltage predicted value and the voltage, obtaining an SOC correction coefficient through the filtering observer;
and correcting the priori SOC value based on the SOC correction coefficient to obtain the true SOC estimated value of the power battery.
Specifically, when the current and the voltage meet the preset dynamic change conditions, the dynamic voltage correction method is judged to be adopted at the moment, namely, the model parameters of the equivalent circuit model are obtained through the obtained parameter information of the power battery, the obtained model parameters are used as input, the voltage predicted value estimated by the equivalent circuit model is obtained through the equivalent circuit model, and the SOC correction coefficient is obtained through the voltage predicted value estimated by the equivalent circuit model and the current voltage of the power battery. And meanwhile, the priori SOC value of the filter observer is obtained by an ampere-hour integration method, and the priori SOC value is corrected by an SOC correction coefficient to obtain the true SOC estimated value of the power battery.
In some embodiments, further comprising:
when the parameter information is effective and the first correction mode and the Kalman filtering method are not satisfied, an ampere-hour integration method is applied to obtain the SOC real estimated value; or alternatively
And when the voltage and the temperature are abnormal and the current is effective, obtaining the SOC real estimated value by applying the ampere-hour integration method.
Specifically, when the parameter information is valid and does not meet the first correction mode and the Kalman filtering method, an ampere-hour integration method is applied to obtain the real estimation value of the SOC. Or when the voltage and the temperature are abnormal and the current is effective, an ampere-hour integration method is also applied to obtain the true estimated value of the SOC. That is, the ampere-hour integration method is used as a final bottom protection correction mode.
In some embodiments, when the voltage and the temperature are abnormal and the current is effective, applying an ampere-hour integration method to obtain the SOC real estimation value, further comprising:
and if the current is abnormal, taking the SOC real estimated value of the last power battery as the SOC real estimated value.
Specifically, if the current is abnormal, the SOC real estimated value of the last power battery is used as the SOC real estimated value, that is, the SOC real estimated value estimated before the current is not abnormal is used as the SOC real estimated value after the current correction.
In some embodiments, after obtaining the SOC real estimation value of the power battery, the method further includes:
based on a preset SOC update rate factor, establishing a corresponding relation between an SOC output variable and an SOC true estimated value of the power battery;
and displaying the SOC output variable.
Specifically, through a preset SOC update rate factor, a corresponding relation between an SOC output variable and an SOC true estimated value of the power battery is established, and the method specifically comprises the following steps:
SOC_out(k)＝SOC_out(k-1)+Speed(k)*delta_SOC_act(k)；
speed (k) =Φ1×delta_soc_diff (k) +Φ2×delta_soc_act (k) +Φ3×delta_soc_thre+Φ 4*T actual measurement (k);
delta_SOC_diff(k)＝SOC_real(k)-SOC_out(k-1)；
wherein k and k-1 represent two adjacent moments;
soc_out represents the SOC output variable;
SOC_real represents the SOC real estimation value;
speed stands for SOC update rate factor;
delta_SOC_act represents the actual SOC variation between two adjacent times;
delta_SOC_diff represents the difference between the SOC_real at the current time and the SOC_out at the previous time;
delta_SOC_thre represents the maximum variation threshold of the SOC between two adjacent time instants;
t is actually measured;
phi 1, phi 2, phi 3, phi 4 represent the correlation coefficients.
After the SOC output variable is calculated, the SOC output variable is displayed through a display screen. The influence of abrupt change on the whole BMS control strategy can be prevented while the continuous correction of the SOC_out can be ensured through the preset SOC update rate factor.
The application also discloses an SOC estimation system of the power battery, which comprises: the SOC estimation method of the power battery in the above embodiment is implemented by a processor and a memory in which at least one instruction, at least one program, a code set, or an instruction set is stored, the instruction, the program, the code set, or the instruction set being loaded by the processor and executed.
The specific manner in which the operations are performed in relation to the systems of the above embodiments has been described in detail in relation to the embodiments of the method and will not be described in detail herein.
Referring now to fig. 2, a vehicle is provided. Fig. 2 shows a schematic diagram of a vehicle structure of an SOC estimation system suitable for use in implementing the power cell of an embodiment of the present application. Comprising a processor and a memory, the processor and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the steps of: acquiring parameter information of a power battery, wherein the parameter information at least comprises current, voltage and temperature; judging whether a first correction mode is met or not based on the change conditions of the current, the voltage and the temperature under the condition that the parameter information is effective; if so, correcting the real SOC estimation value of the power battery to a corresponding preset value in the first correction mode according to the parameter range of the parameter information; if not, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery through a Kalman filtering method based on a voltage predicted value and the voltage, wherein the voltage predicted value is predicted based on an equivalent circuit model.
In the embodiments of the present application, the processor is a processing device that performs logic 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 processor (GPU), or the like, and has data processing capability and/or program execution capability. It will be readily appreciated that the processor is typically communicatively coupled to a memory, on which is stored any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), USB memory, flash memory, and the like. One or more computer instructions may be stored on the memory and executed by the processor to perform the relevant analysis functions. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer readable storage medium.
In the embodiment of the application, each module may run on the same processor or may run on multiple processors; the modules may be run on processors of the same architecture, e.g., all on processors of the X86 system, or on processors of different architectures, e.g., the image processing module runs on the CPU of the X86 system and the machine learning module runs on the GPU. The modules may be packaged in one computer product, for example, the modules are packaged in one computer software and run in one computer (server), or may be packaged separately or partially in different computer products, for example, the image processing modules are packaged in one computer software and run in one computer (server), and the machine learning modules are packaged separately in separate computer software and run in another computer (server); the computing platform when each module executes may be local computing, cloud computing, or hybrid computing composed of local computing and cloud computing.
As shown in fig. 2, the computer system includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for operation instructions of the system are also stored. The CPU301, ROM302, and RAM303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305; an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The 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, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.
The present application also provides a non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the steps of: acquiring parameter information of a power battery, wherein the parameter information at least comprises current, voltage and temperature; judging whether a first correction mode is met or not based on the change conditions of the current, the voltage and the temperature under the condition that the parameter information is effective; if so, correcting the real SOC estimation value of the power battery to a corresponding preset value in the first correction mode according to the parameter range of the parameter information; if not, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery through a Kalman filtering method based on a voltage predicted value and the voltage, wherein the voltage predicted value is predicted based on an equivalent circuit model.
In one embodiment, a computer program product is provided, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the steps of: acquiring parameter information of a power battery, wherein the parameter information at least comprises current, voltage and temperature; judging whether a first correction mode is met or not based on the change conditions of the current, the voltage and the temperature under the condition that the parameter information is effective; if so, correcting the real SOC estimation value of the power battery to a corresponding preset value in the first correction mode according to the parameter range of the parameter information; if not, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery through a Kalman filtering method based on a voltage predicted value and the voltage, wherein the voltage predicted value is predicted based on an equivalent circuit model.
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 two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (12)
1. A SOC estimation method of a power battery, comprising:
acquiring parameter information of a power battery, wherein the parameter information at least comprises current, voltage and temperature;
judging whether a first correction mode is met or not based on the change conditions of the current, the voltage and the temperature under the condition that the parameter information is effective;
if so, correcting the real SOC estimation value of the power battery to a corresponding preset value in the first correction mode according to the parameter range of the parameter information;
if not, and when the change conditions of the current and the voltage meet the preset dynamic change conditions, obtaining an SOC real estimated value of the power battery through a Kalman filtering method based on a voltage predicted value and the voltage, wherein the voltage predicted value is predicted based on an equivalent circuit model.
2. The SOC estimation method of a power battery according to claim 1, wherein the current and the voltage satisfy a preset dynamic change condition when the following condition is satisfied, comprising:
the change amount of the current is not smaller than a first threshold value in a first preset time, the power battery is in a non-charging state, and the change direction of the current is opposite to the change direction of the voltage in the first preset time; or,
and under the condition that the real SOC estimated value of the last power battery is obtained by a Kalman filtering method, the parameter information is valid at the current moment, the power battery is in a non-charging state, and the duration that the variation of the current is smaller than a second threshold value is smaller than a second preset time.
3. The SOC estimation method of claim 1 or 2, wherein the obtaining the SOC true estimation value 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;
applying an ampere-hour integration method to obtain a priori SOC value of the filtering observer;
obtaining a voltage predicted value estimated by the equivalent circuit model based on model parameters of the equivalent circuit model;
based on the voltage predicted value and the voltage, obtaining an SOC correction coefficient through the filtering observer;
and correcting the priori SOC value based on the SOC correction coefficient to obtain the true SOC estimated value of the power battery.
4. The SOC estimation method of claim 1, wherein if yes, correcting the SOC real estimation value of the power battery to a corresponding preset value by the first correction method according to the parameter range of the parameter information, includes:
correcting the SOC real estimation value to a first preset value when the following conditions are met in the first preset time:
the absolute value of the current is not greater than a third threshold, the voltage is not less than an upper cutoff voltage, and the temperature is not less than a fourth threshold.
5. The SOC estimation method of the power battery according to claim 4, further comprising:
correcting the SOC real estimation value to a second preset value when the following conditions are met within the first preset time:
the absolute value of the current is not greater than a third threshold, the voltage is not greater than a lower cutoff voltage, and the temperature is not less than a fourth threshold.
6. The SOC estimation method of the power battery according to claim 4, further comprising:
obtaining the SOC real estimation value based on an open circuit voltage method when the following conditions are satisfied:
the absolute value of the current is not greater than a fifth threshold value, and the duration of the absolute value of the current not greater than the fifth threshold value is not less than a preset duration at the weighted temperature of the power battery.
7. The SOC estimation method of the power battery according to claim 1, further comprising:
when the parameter information is effective and the first correction mode and the Kalman filtering method are not satisfied, an ampere-hour integration method is applied to obtain the SOC real estimated value; or alternatively
And when the voltage and the temperature are abnormal and the current is effective, obtaining the SOC real estimated value by applying the ampere-hour integration method.
8. The SOC estimation method of claim 7, wherein when the voltage and temperature are abnormal and the current is valid, the ampere-hour integration method is applied to obtain the SOC true estimation value, further comprising:
and if the current is abnormal, taking the SOC real estimated value of the last power battery as the SOC real estimated value.
9. The SOC estimation method of the power battery according to claim 1, further comprising, after obtaining the SOC true estimation value of the power battery:
based on a preset SOC update rate factor, establishing a corresponding relation between an SOC output variable and an SOC true estimated value of the power battery;
and displaying the SOC output variable.
10. An SOC estimation system of a power battery, comprising: a processor and a memory, in which at least one instruction, at least one program, a code set, or an instruction set is stored, the instruction, the program, the code set, or the instruction set being loaded and executed by the processor to implement the SOC estimation method of the power battery according to claims 1 to 9.
11. A vehicle, characterized by comprising: a processor and a memory, in which at least one instruction, at least one program, a code set, or an instruction set is stored, the instruction, the program, the code set, or the instruction set being loaded and executed by the processor to implement the SOC estimation method of the power battery according to claims 1 to 9.
12. A non-transitory computer readable storage medium, characterized in that instructions in the non-transitory computer readable storage medium, when executed by a processor of a vehicle, enable the vehicle to perform the SOC estimation method of the power battery according to any one of claims 1-9.
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