CN111289911A - SOC estimation method and device based on battery and electronic equipment - Google Patents
SOC estimation method and device based on battery and electronic equipment Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The application provides a battery-based SOC estimation method and device and electronic equipment, relates to the technical field of battery management, and solves the technical problem that the existing SOC estimation has great dependence on CPU calculation power. The method comprises the following steps: obtaining an SOC predicted value of the battery based on a first-order RC equivalent circuit model of the battery; obtaining an SOC observation value based on the integration of the battery passing current ampere-hour; calculating through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value to obtain a Kalman gain coefficient; and correcting the SOC observation value by using the Kalman gain coefficient to obtain a final SOC estimation value of the battery.
Description
Technical Field
The present disclosure relates to the field of battery management technologies, and in particular, to a battery-based SOC estimation method and apparatus, and an electronic device.
Background
At present, the oil consumption of a vehicle is directly related to the running mode of the vehicle, a 48V start-stop system can realize the start-stop and braking energy recovery of an engine, the modification difficulty is small, the cost is low, the energy conservation of 15-20% and the emission reduction of 10-15% are expected to be realized, the method is one of the most practical technical schemes, and the method is favorable for upgrading and upgrading the traditional vehicle under the new emission standard to meet the requirements.
The battery management system is a controller of a battery PCAK and is used for managing information such as voltage, temperature, current and the like of a battery module, managing the charging and discharging process of the battery module and interacting with the controller and a vehicle control unit so as to achieve maximum fuel economy. The State of Charge (SOC) of a battery is also called the remaining battery capacity, SOC estimation is one of the most important functions in a battery management system, and many other functions in the battery management system depend on the result of SOC estimation.
The current SOC estimation utilizes an iterative equation of a covariance matrix which has a high requirement on model accuracy and Central Processing Unit (CPU) computation power, and thus has a high dependence on CPU computation power.
Disclosure of Invention
The invention aims to provide a battery-based SOC estimation method, a battery-based SOC estimation device and electronic equipment, so as to relieve the technical problem that the existing SOC estimation has great dependence on CPU calculation power.
In a first aspect, an embodiment of the present application provides a battery-based SOC estimation method, which is applied to a management system of a battery, and the method includes:
obtaining an SOC predicted value of the battery based on a first-order RC equivalent circuit model of the battery;
obtaining an SOC observation value based on the integration of the battery passing current ampere-hour;
calculating through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value to obtain a Kalman gain coefficient;
and correcting the SOC observation value by using the Kalman gain coefficient to obtain a final SOC estimation value of the battery.
In one possible implementation, the step of obtaining the SOC prediction value of the battery based on a first-order RC equivalent circuit model of the battery includes:
determining an initial value of the SOC of the battery according to a first-order RC equivalent circuit model of the battery;
and obtaining the SOC predicted value of the battery through a measurement equation based on the SOC rated capacity value of the battery and the SOC initial value.
In one possible implementation, the step of determining an initial value of the SOC of the battery according to a first-order RC equivalent circuit model of the battery includes:
acquiring a first sub open-circuit voltage measured value of the battery and a second sub open-circuit voltage measured value before the current moment stored in an Electrically Erasable Programmable Read Only Memory (EEPROM);
determining an initial value of SOC of the battery from the first and second sub-open-circuit voltage measurements according to a rest time of an open-circuit voltage of the battery.
In one possible implementation, before the step of obtaining the predicted SOC value of the battery through a measurement equation based on the rated SOC capacity value and the initial SOC value of the battery, the method further includes:
obtaining the battery cell aging degree Of the battery based on the percentage (State Of Health, SOH for short) Of the full charge capacity Of the storage battery Of the battery relative to the rated capacity;
and determining the SOC rated capacity value of the battery according to the cell aging degree.
In one possible implementation, the step of deriving the SOC observation based on an integration of the battery passing current ampere-hour comprises:
and obtaining the SOC observed value of the battery through an observation equation based on the integral of the battery passing current ampere-hour, the temperature value and the current value of the preset sampling frequency.
In one possible implementation, the method further comprises:
identifying by a least square method according to Hybrid pulse power characteristics (HPPC for short) test data of the battery to obtain identification parameters;
determining the observation equation and the measurement equation based on a plurality of the identified parameters.
In one possible implementation, after the step of correcting the SOC observation value by using the kalman gain coefficient, the method further includes:
and updating the weight of the SOC predicted value and the weight of the SOC observation value by using the Kalman gain coefficient to obtain the updated weight of the SOC predicted value and the updated weight of the SOC observation value.
In a second aspect, a battery-based SOC estimation device is provided, which is applied to a management system of the battery, and includes:
the first acquisition module is used for acquiring an SOC predicted value of the battery based on a first-order RC equivalent circuit model of the battery;
the second acquisition module is used for obtaining an SOC observation value based on the integration of the battery passing current ampere-hour;
the calculation module is used for calculating through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value to obtain a Kalman gain coefficient;
and the correction module is used for correcting the SOC observation value by using the Kalman gain coefficient to obtain the final SOC estimation value of the battery.
In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements the method of the first aspect when executing the computer program.
In a fourth aspect, this embodiment of the present application further provides a computer-readable storage medium storing machine executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method of the first aspect.
The embodiment of the application brings the following beneficial effects:
according to the SOC estimation method, the SOC estimation device and the electronic equipment based on the battery, the SOC predicted value of the battery can be obtained based on a first-order RC equivalent circuit model of the battery, the SOC observation value is obtained based on the battery through integration of current ampere-hour, calculation is carried out through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value, a Kalman gain coefficient is obtained, and finally the SOC observation value is corrected through the Kalman gain coefficient, so that the SOC final estimation value of the battery is obtained.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a flow chart of a battery-based SOC estimation method provided by an embodiment of the present application;
fig. 2 is a schematic diagram illustrating measurement of an open-circuit voltage in a method for estimating a battery-based SOC according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an equivalent circuit model in the method for estimating SOC based on a battery according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of a battery-based SOC estimation apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram illustrating an electronic device provided in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "comprising" and "having," and any variations thereof, as referred to in the embodiments of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Currently, SOC estimation is a challenging task, and more than half of the documents published in recent years about battery management systems discuss SOC estimation. The current SOC estimation utilizes an iterative equation of a covariance matrix with high requirements on model accuracy and CPU computational power, and thus has a high dependence on the CPU computational power.
Based on this, the embodiments of the present application provide a battery-based SOC estimation method, device and electronic device, by which the technical problem that the current SOC estimation depends greatly on the CPU computation power can be alleviated.
Embodiments of the present invention are further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a method for estimating SOC based on a battery according to an embodiment of the present disclosure. The method is applied to a management system of the battery. As shown in fig. 1, the method includes:
and step S110, obtaining the SOC predicted value of the battery based on the first-order RC equivalent circuit model of the battery.
And step S120, obtaining an SOC observation value based on the battery passing current ampere-hour integration.
And S130, calculating through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value to obtain a Kalman gain coefficient.
The weight η (t is 0) of the predicted value of the SOC is the highest, and decreases with the increase of t, the weight of the observed value of the SOC is the lowest with δ (t is 0), and gradually increases with the increase of t.
And S140, correcting the SOC observation value by using the Kalman gain coefficient to obtain the final SOC estimation value of the battery.
In this step, the estimated value of the SOC can be observed according to the kalman gain correction state, thereby obtaining the final estimated value of the SOC of the battery.
The SOC estimation method based on the battery is an SOC algorithm which is easy to realize on the basis of low error and adaptability, can be suitable for an SOC algorithm of a 48V HEV vehicle type, can reduce dependence on CPU calculation power, and reduces battery cell test data volume, and therefore can be suitable for a certain type of battery cell platform of a lithium battery.
By providing a fuzzy logic based Kalman filtering SOC algorithm, using selection fuzzy logic, the fuzzy logic and Kalman filtering are effectively fused, a simple method for extracting an exact conclusion from ambiguous or inaccurate information is used, and system characteristics are represented by higher abstraction of expert knowledge and experience. Therefore, an iterative equation of a covariance matrix with high requirements on model precision and CPU computing power is replaced, the Kalman filtering is strong in robustness, low in error and high in reliability, and meanwhile the defects that the Kalman filtering has high requirements on the CPU computing power and the model precision are overcome.
Therefore, the SOC estimation algorithm based on the Kalman filtering of the fuzzy logic in the method provided by the embodiment of the application can overcome the defects of poor reliability, large error, weak robustness and the like in the prior art, overcomes the defects of serious dependence on large CPU computing power and test data quantity and the like, and has high robustness, low error and high reliability.
The above steps are described in detail below.
In some embodiments, the step S110 may include the following steps:
step a), determining an initial value of the SOC of the battery according to a first-order RC equivalent circuit model of the battery;
and b), obtaining the SOC predicted value of the battery through a measurement equation based on the SOC rated capacity value and the SOC initial value of the battery.
Here, the initial SOC value is a measurement result value based on the OCV (open circuit voltage). Under the condition that the initial value SOC is accurate and the rated capacity is known, the measured SOC obtained by the measurement equation in a short time t is reliable.
Based on the above steps a) and b), the above step a) may include the following steps:
step c), acquiring a first sub open-circuit voltage measured value of the battery and a second sub open-circuit voltage measured value before the current moment stored in the EEPROM;
and d), determining an initial SOC value of the battery from the first sub-open-circuit voltage measurement value and the second sub-open-circuit voltage measurement value according to the standing time of the open-circuit voltage of the battery.
As shown in fig. 2, an initial SOC value of a measurement circuit for measuring an Open Circuit Voltage (OCV) is a measurement result value of the OCV and an SOC value stored in a previous time of an Electrically Erasable Programmable Read Only Memory (EEPROM).
In practical applications, the OCV is taken as the result value if the OCV satisfies the standing time. Otherwise, the SOC value of the EEPROM is used. Thus, it can be ensured that the initial value is as close as possible to the true value.
Based on the above steps a) and b), before the above step b), the method may further include the steps of:
step e), obtaining the cell aging degree of the battery based on the SOH of the battery;
and f), determining the SOC rated capacity value of the battery according to the aging degree of the battery core.
The percentage Of the full charge capacity Of the battery to the rated capacity (SOH) refers to the battery capacity, Health, and performance status. In this step, the SOH module outputs the aging degree of the battery cell to provide a rated capacity value for the SOC module.
In some embodiments, the step S120 may include the following steps:
and g), obtaining the SOC observed value of the battery through an observation equation based on the integral of the current ampere hour of the battery, and the temperature value and the current value of the preset sampling frequency.
For the observation equation, for example, the temperature and current at the sampling frequency of 100ms are used as system excitation, the working voltage of the battery is used as an observation variable, and the state parameters in the model are identified, so that the observation SOC is obtained.
Based on the above steps b) and g), the method may further comprise the steps of:
step h), identifying the HPPC test data of the battery by a least square method to obtain identification parameters;
step i), determining an observation equation and a measurement equation based on the plurality of identification parameters.
As shown in fig. 3, the SOC algorithm is established on the basis of a first-order equivalent circuit model, and parameters Rp, Cp, Re, Voc and the like are obtained by identifying data of a Hybrid pulse power characteristics (HPPC for short) test by using a least square method, so as to obtain an observation equation and a measurement equation.
In some embodiments, after the step S140, the method may further include the steps of:
and j), updating the weight of the SOC predicted value and the weight of the SOC observation value by using the Kalman gain coefficient to obtain the updated weight of the SOC predicted value and the updated weight of the SOC observation value.
In practical application, the estimated value of the SOC can be observed according to the Kalman gain correction state, and delta (t) and η (t) are updated at the same time, so that a Kalman filtering SOC estimation algorithm based on fuzzy logic is realized.
Fig. 4 provides a schematic diagram of a battery-based SOC estimation apparatus. The device is applied to a management system of the battery. As shown in fig. 4, the battery-based SOC estimation apparatus 400 includes:
the first obtaining module 401 is configured to obtain a predicted SOC value of the battery based on a first-order RC equivalent circuit model of the battery;
a second obtaining module 402, configured to obtain an SOC observation value based on an integration of a battery passing current ampere-hour;
the calculating module 403 is configured to calculate through a kalman filter algorithm according to the weight of the SOC predicted value and the weight of the SOC observed value, so as to obtain a kalman gain coefficient;
and a correcting module 404, configured to correct the SOC observation value by using the kalman gain coefficient, so as to obtain a final SOC estimation value of the battery.
In some embodiments, the first obtaining module 401 is specifically configured to:
determining an initial value of the SOC of the battery according to a first-order RC equivalent circuit model of the battery;
and obtaining the SOC predicted value of the battery through a measurement equation based on the SOC rated capacity value and the SOC initial value of the battery.
In some embodiments, the first obtaining module 401 is further configured to:
acquiring a first sub open-circuit voltage measured value of the battery and a second sub open-circuit voltage measured value before the current moment stored in the EEPROM;
determining an initial value of the SOC of the battery from the first and second sub-open-circuit voltage measurement values according to a rest time of an open-circuit voltage of the battery.
In some embodiments, the first obtaining module 401 is further configured to:
obtaining the cell aging degree of the battery based on the SOH of the battery;
and determining the SOC rated capacity value of the battery according to the aging degree of the battery core.
In some embodiments, the second obtaining module 402 is specifically configured to:
and obtaining the SOC observed value of the battery through an observation equation based on the integral of the current ampere-hour of the battery and the temperature value and the current value of the preset sampling frequency.
In some embodiments, the apparatus further comprises:
the identification module is used for identifying through a least square method according to HPPC test data of the battery to obtain identification parameters;
a determination module to determine an observation equation and a measurement equation based on the plurality of identification parameters.
In some embodiments, the apparatus further comprises:
and the updating module is used for updating the weight of the SOC predicted value and the weight of the SOC observed value by using the Kalman gain coefficient to obtain the updated weight of the SOC predicted value and the updated weight of the SOC observed value.
The SOC estimation device based on the battery provided by the embodiment of the present application has the same technical features as the SOC estimation method based on the battery provided by the above embodiment, so the same technical problems can be solved, and the same technical effects can be achieved.
As shown in fig. 5, an electronic device 500 includes a memory 501 and a processor 502, where the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method provided in the foregoing embodiment.
Referring to fig. 5, the electronic device further includes: a bus 503 and a communication interface 504, and the processor 502, the communication interface 504 and the memory 501 are connected by the bus 503; the processor 502 is for executing executable modules, e.g. computer programs, stored in the memory 501.
The Memory 501 may include a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 504 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
Bus 503 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
The memory 501 is used for storing a program, and the processor 502 executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the process disclosed in any of the foregoing embodiments of the present application may be applied to the processor 502, or implemented by the processor 502.
The processor 502 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 502. The Processor 502 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 501, and the processor 502 reads the information in the memory 501, and completes the steps of the method in combination with the hardware thereof.
In accordance with the above-described battery-based SOC estimation method, embodiments of the present application further provide a computer-readable storage medium storing machine-executable instructions that, when invoked and executed by a processor, cause the processor to perform the steps of the above-described battery-based SOC estimation method.
The battery-based SOC estimation apparatus provided in the embodiments of the present application may be specific hardware on a device, or software or firmware installed on a device, or the like. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
For another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the battery-based SOC estimation method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A battery-based SOC estimation method, applied to a management system of the battery, the method comprising:
obtaining an SOC predicted value of the battery based on a first-order RC equivalent circuit model of the battery;
obtaining an SOC observation value based on the integration of the battery passing current ampere-hour;
calculating through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value to obtain a Kalman gain coefficient;
and correcting the SOC observation value by using the Kalman gain coefficient to obtain a final SOC estimation value of the battery.
2. The method of claim 1, wherein the step of deriving the predicted SOC value for the battery based on a first order RC equivalent circuit model of the battery comprises:
determining an initial value of the SOC of the battery according to a first-order RC equivalent circuit model of the battery;
and obtaining the SOC predicted value of the battery through a measurement equation based on the SOC rated capacity value of the battery and the SOC initial value.
3. The method of claim 2, wherein the step of determining an initial value of the SOC of the battery based on a first order RC equivalent circuit model of the battery comprises:
acquiring a first sub open-circuit voltage measured value of the battery and a second sub open-circuit voltage measured value before the current moment stored in the EEPROM;
determining an initial value of SOC of the battery from the first and second sub-open-circuit voltage measurements according to a rest time of an open-circuit voltage of the battery.
4. The method of claim 2, wherein the step of obtaining the predicted SOC value of the battery by a measurement equation based on the SOC rated capacity value and the initial SOC value further comprises:
obtaining the cell aging degree of the battery based on the SOH of the battery;
and determining the SOC rated capacity value of the battery according to the cell aging degree.
5. The method of claim 2, wherein the step of deriving the SOC observation based on an integration of the battery through current ampere-hour comprises:
and obtaining the SOC observed value of the battery through an observation equation based on the integral of the battery passing current ampere-hour, the temperature value and the current value of the preset sampling frequency.
6. The method of claim 5, further comprising:
identifying the HPPC test data of the battery by a least square method to obtain identification parameters;
determining the observation equation and the measurement equation based on a plurality of the identified parameters.
7. The method of claim 1, wherein the step of modifying the SOC observation using the Kalman gain coefficient is followed by further comprising:
and updating the weight of the SOC predicted value and the weight of the SOC observation value by using the Kalman gain coefficient to obtain the updated weight of the SOC predicted value and the updated weight of the SOC observation value.
8. A battery-based SOC estimation device, characterized in that, applied to a management system of the battery, it comprises:
the first acquisition module is used for acquiring an SOC predicted value of the battery based on a first-order RC equivalent circuit model of the battery;
the second acquisition module is used for obtaining an SOC observation value based on the integration of the battery passing current ampere-hour;
the calculation module is used for calculating through a Kalman filtering algorithm according to the weight of the SOC predicted value and the weight of the SOC observation value to obtain a Kalman gain coefficient;
and the correction module is used for correcting the SOC observation value by using the Kalman gain coefficient to obtain the final SOC estimation value of the battery.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and wherein the processor implements the steps of the method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to execute the method of any of claims 1 to 7.
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