CN115754755A - Method and device for estimating state of charge of battery - Google Patents
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Abstract
A method and apparatus for estimating state of charge of a battery, the method comprising: establishing a battery model for a battery, and establishing an algorithm estimation model based on the relation between the battery state of charge and the open-circuit voltage of the battery; obtaining an estimated terminal voltage based on the battery model and the algorithm estimation model, and performing iterative correction on the algorithm estimation model based on a difference value between the estimated terminal voltage and an actual terminal voltage; in the iterative correction process, an estimated battery state of charge is obtained based on the algorithm estimation model, when the estimated battery state of charge enters a preset range, a weight coefficient is introduced into the iterative correction, the iterative correction is carried out on the algorithm estimation model based on an adjusted difference value, and the weight coefficient is larger than 1. The method and the device can improve the convergence rate of the algorithm estimation model, so that the correction capability of estimating the initial error of the battery state of charge is improved.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to a method and a device for estimating the state of charge of a battery.
Background
The State of charge (SOC) of the battery represents the percentage of the remaining available electric quantity of the battery to the total capacity, is one of the most important states in a battery management system, and provides important references for the functions of battery safety management, charge and discharge control, vehicle energy management and the like of the electric vehicle. The SOC is used as a key parameter of a Battery Management System (BMS), cannot be directly measured, and can only be estimated by using information such as voltage, current, temperature and the like in combination with different types of Battery models and algorithm estimators under different working conditions. Current SOC estimation schemes cannot quickly converge to the initial SOC error.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description. The summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In view of the deficiencies of the prior art, a first aspect of the embodiments of the present invention provides a method for estimating a state of charge of a battery, including: establishing a battery model for a battery, and establishing an algorithm estimation model based on the relation between the battery charge state and the open-circuit voltage of the battery; obtaining an estimated terminal voltage based on the battery model and the algorithm estimation model, and performing iterative correction on the algorithm estimation model based on a difference value between the estimated terminal voltage and an actual terminal voltage; in the iterative correction process, the estimated battery state of charge is obtained based on the algorithm estimation model, when the estimated battery state of charge enters a preset range, the difference value is adjusted based on a weight coefficient, the iterative correction is carried out on the algorithm estimation model based on the adjusted difference value, and the weight coefficient is larger than 1.
In one embodiment, the method further comprises: and determining the weight coefficient in real time according to the working condition, the temperature condition and/or the aging condition of the battery.
In one embodiment, the predetermined range is determined according to a modifiable region in a battery state of charge versus open circuit voltage curve.
In one embodiment, the preset range includes at least one of: 98% -100%, 58% -65% and 0-35%.
In one embodiment, the algorithmic estimation model comprises a kalman filter.
In one embodiment, the kalman filter derives the estimated battery state of charge at the current time from the sum of: an estimated battery state of charge at a previous time, and a product of the difference and a gain; the introducing of the weight coefficient in the iterative modification comprises: multiplying the product of the difference and the gain by the weight coefficient.
In one embodiment, the battery model comprises an equivalent circuit model.
In one embodiment, deriving an estimated terminal voltage based on the battery model and the algorithmic estimation model comprises: obtaining an estimated open circuit voltage corresponding to the estimated battery state of charge based on the algorithm estimation model; and taking the current signal, the voltage signal and the temperature signal of the battery as input signals, inputting the input signals and the estimated open-circuit voltage into the equivalent circuit model after parameter identification, and outputting the estimated terminal voltage.
In one embodiment, the battery comprises a lithium iron phosphate battery.
A second aspect of the embodiments of the present invention provides a battery state of charge estimation apparatus, which includes a memory and a processor, where the memory stores thereon a computer program executed by the processor, and the computer program, when executed by the processor, executes the battery state of charge estimation method described above.
According to the method and the device for estimating the state of charge of the battery, disclosed by the embodiment of the invention, when the estimated state of charge of the battery enters the preset range, the weight coefficient is introduced to adjust the difference value between the estimated terminal voltage and the actual terminal voltage, and the algorithm estimation model is subjected to iterative correction based on the adjusted difference value, so that the convergence speed of the algorithm estimation model can be increased, and the correction capability of the initial error of the estimated state of charge of the battery is improved.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail embodiments of the present invention with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally represent like parts or steps.
FIG. 1 is a schematic flow diagram of a method of estimating battery state of charge according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a battery model and an algorithmic estimation model in accordance with one embodiment of the invention;
FIG. 3 is a plot of battery state of charge versus open circuit voltage according to one embodiment of the present invention;
FIG. 4 is a comparison graph of estimation accuracy over the range of 85% -50% for a method of estimating state of charge of a battery according to one embodiment of the invention;
FIG. 5 is a comparison graph of estimation accuracy over the range of 85% -10% for a method of estimating state of charge of a battery according to one embodiment of the invention;
fig. 6 is a schematic block diagram of a battery state of charge estimation apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the application described in the application without inventive step, shall fall within the scope of protection of the application.
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art, that the present application may be practiced without one or more of these specific details. In other instances, well-known features of the art have not been described in order to avoid obscuring the present application.
It is to be understood that the present application is capable of implementation in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.
In order to provide a thorough understanding of the present application, a detailed structure will be presented in the following description in order to explain the technical solutions presented in the present application. Alternative embodiments of the present application are described in detail below, however, the present application may have other implementations in addition to these detailed descriptions.
An existing estimation method for a battery state of charge is to calibrate an error introduced by ampere-hour integration according to a corresponding relationship between Open Circuit Voltage (OCV) and the battery state of charge (SOC), so as to improve estimation accuracy of the SOC, but the scheme cannot rapidly converge an initial SOC error. Particularly for lithium iron phosphate (LEP) batteries, the SOC-OCV curve of LFP batteries has a long plateau region. In the plateau region, since one OCV may correspond to a plurality of SOC values, thereby introducing an estimation error, SOC correction using the SOC-OCV curve cannot be performed in the plateau region. The SOC correctable area of the LFP battery can be divided into an area A, an area B and an area C, wherein the area A is nearly fully charged, and the algorithm triggering probability is low; the sum of the areas B and C is 42% and 7% respectively, but the possibility of discharging to 10% or less is low due to mileage anxiety common to the owner of the electric vehicle, resulting in further reduction of the correctable area of the SOC. Due to the limited correctable region, the initial SOC error requires multiple discharge cycles to gradually converge to a reasonable value.
Based on this, the embodiment of the present invention provides a method and an apparatus for estimating a state of charge of a battery, which correspondingly output a set of weight coefficients in a correctable region, and adjust an information (a difference between an estimated terminal voltage and an actual terminal voltage) through the weight coefficients, thereby improving a convergence speed and enhancing a correction capability of an algorithm estimation model.
The following describes a method for estimating a battery state of charge and an apparatus for estimating a battery state of charge according to an embodiment of the present invention with reference to the drawings. Referring initially to fig. 1, fig. 1 illustrates a schematic flow diagram of a method 100 for estimating battery state of charge in accordance with an embodiment of the present invention. As shown in fig. 1, the method 100 for estimating the state of charge of the battery includes:
in step S110, a battery model is established for a battery, and an algorithm estimation model is established based on a relationship between a battery state of charge and an open circuit voltage of the battery;
in step S120, obtaining an estimated terminal voltage based on the battery model and the algorithm estimation model, and iteratively correcting the algorithm estimation model based on a difference between the estimated terminal voltage and an actual terminal voltage;
in step S130, in the iterative correction process, an estimated battery state of charge is obtained based on the algorithm estimation model, when the estimated battery state of charge enters a preset range, the difference is adjusted based on a weight coefficient, and the iterative correction is performed on the algorithm estimation model based on the adjusted difference, where the weight coefficient is greater than 1.
Illustratively, the battery to which the battery state of charge estimation method 100 relates includes a lithium iron phosphate battery. As shown in fig. 3, a relation curve of the state of charge and the open-circuit voltage of the lithium iron phosphate battery includes a longer platform region, and the estimated state of charge of the battery cannot be corrected in the platform region according to the relation between the state of charge and the open-circuit voltage, so that the initial SOC error can gradually converge only after multiple discharge cycles. For the lithium iron phosphate battery, the preset range is determined according to correctable areas (namely, an area A, an area B and an area C in fig. 3) in a relation curve of the state of charge and the open-circuit voltage of the battery, and the convergence rate of iterative correction can be improved by adjusting the difference value between the estimated terminal voltage and the actual terminal voltage in the correctable areas based on the weight coefficient, so that the correction capability of the initial estimated battery state of charge error is improved.
The algorithm estimation model adopted by the embodiment of the invention includes, but is not limited to, a Kalman filter, and the battery model includes, but is not limited to, an equivalent circuit model. Compared with other algorithm estimation models, the Kalman filter is beneficial to correcting the initial value and weakening the influence of electromagnetic interference. Kalman filtering also includes extended kalman filtering, unscented kalman filtering, volumetric kalman filtering, and the like. The Kalman filter continuously estimates the SOC value of the next moment according to the SOC value of the previous moment. The Kalman filter is adopted to estimate the SOC value, so that the noise error in the process can be gradually reduced, the error divergence phenomenon generated by iteration is improved, and the estimation precision of the SOC value is improved.
For the battery model, an electrochemical model, a thermodynamic model, a coupling model, an equivalent circuit model, and the like can be used in the mechanical modeling selection. In a specific example, the battery model may adopt an Equivalent Circuit Model (ECM), in contrast, the equivalent circuit model does not need to perform deep analysis on the electrochemical reaction inside the battery, but describes the open-circuit voltage, the direct-current internal resistance and the polarization internal resistance of the battery through a circuit so as to realize the characterization of the external characteristics of the battery.
For example, the equivalent circuit model may include a first-order RC equivalent circuit model, a second-order RC equivalent circuit model, or a multi-order RC equivalent circuit model, which is not limited in this embodiment of the present invention.
Referring to fig. 2, when the battery model is an equivalent circuit model and the algorithm estimation model is a kalman filter, obtaining an estimated terminal voltage based on the battery model and the algorithm estimation model includes: obtaining an estimated open-circuit voltage corresponding to the estimated battery state of charge based on an algorithm estimation model; and taking the current signal, the voltage signal and the temperature signal of the battery as input signals, inputting the input signals and the estimated open-circuit voltage into the equivalent circuit model after parameter identification, and outputting the estimated terminal voltage. The embodiment of the invention does not limit the method for identifying the parameters.
The voltage signal input into the battery model is the real terminal voltage (Vt _ act) of the battery, the current signal is the terminal current of the battery, R0 is the ohmic internal resistance value of the battery, and R1 and C1 are the polarization resistance value and the polarization capacitance value of the battery, respectively. The estimated terminal voltage Vt _ est of the battery can be obtained according to the equivalent circuit model, the estimated terminal voltage Vt _ est is obtained according to the open-circuit voltage of the battery and the battery parameters, and the difference value between the actual terminal voltage and the estimated terminal voltage is new Verr (Verr = Vt _ act-Vt _ est).
Since the magnitude of the innovation depends on the open-circuit voltage, and the open-circuit voltage and the estimated battery state of charge output by the kalman filter have a corresponding relationship as shown in fig. 3, the kalman estimator updates the gain L _ gain in each iteration in a loop iteration manner, and iteratively outputs the estimated battery state of charge SOC _ est at the current time, that is, SOC (k) = SOC (k-1) + L _ gain Verr, according to the estimated battery state of charge at the previous time. The Kalman filter estimates the unknown process noise mean value and variance in real time in the process of each iteration, and brings the filter parameters updated in real time into the next calculation again, so that the parameter error of the Kalman filter is gradually reduced along with the increase of the iteration times, the divergence phenomenon of the noise error of the Kalman filter caused by the gradual increase of the iteration is effectively improved, and the estimation precision is improved.
As shown in fig. 3, since the LFP battery SOC-OCV curve has long plateau regions, mainly 35% -58% and 65% -98%, since one OCV may correspond to a plurality of SOC values in the plateau region, SOC correction cannot be performed using the SOC-OCV curve in the plateau region, thereby introducing estimation errors. The preset range of the embodiment of the invention is determined according to the correctable region in the relation curve of the battery state of charge and the open-circuit voltage, and specifically comprises at least one of the region a (98% -100%), the region B (58% -65%) and the region C (0% -35%), and since the platform region cannot perform SOC correction by using the SOC-OCV curve, the correction capability of SOC _ est can be enhanced by introducing a weight coefficient into the correctable region. The OCV-SOC curve can be obtained through a constant-current charge and discharge experiment, and a functional relation expression between the OCV and the SOC is obtained through fitting.
For example, taking the kalman filter as described above as an example, after introducing the weight coefficient, the kalman filter obtains the estimated battery state of charge at the current time according to the sum of: the estimated battery state of charge at the previous moment and the product of the innovation, the weight coefficient and the gain, namely SOC (k) = SOC (k-1) + Wf L _ gain Verr, and then the estimated battery state of charge SOC _ est is output iteratively, wherein Wf represents the weight coefficient.
Because the algorithm estimation model is iteratively corrected according to the innovation, the convergence rate of iterative correction can be improved in the correctable region by introducing the weight coefficient more than 1 on the basis of the innovation. For the B zone located in the middle and high section for a long time, the weight coefficient is introduced into the B zone, so that the correction capability of the initial SOC error can be remarkably improved. For the C region discharged to the low stage, the convergence speed can also be increased, thereby increasing the SOC estimation accuracy at the discharge cutoff. Since the probability of discharging to 0% SOC is extremely low in the actual working condition, the estimation method 100 for the state of charge of the battery provided by the embodiment of the invention has the advantage that the estimation precision is remarkably improved compared with the estimation method without introducing the weight coefficient.
Further, in some embodiments, the weight coefficient is determined in real time, and specifically, the weight coefficient may be determined in real time according to the operating condition, the temperature condition, and/or the aging condition of the battery, so as to avoid over-estimation caused by improper weight coefficient. For a battery of a vehicle, the operating condition may include a road condition. Illustratively, the correspondence between the public conditions, the temperature conditions and/or the battery aging conditions and the weighting coefficients can be calibrated in advance, and the currently applicable weighting coefficients can be determined in real time according to the correspondence.
For example, under low temperature conditions, the deviation of the algorithm estimation model may increase, and therefore, when the temperature falls below a certain preset temperature, the weight coefficient may be increased, thereby improving the accuracy and convergence speed of the algorithm estimation model. Alternatively, the weight coefficient may be increased linearly with a decrease in temperature.
If the deviation between the current working condition and the working condition adopted when the algorithm estimation model is established is large, the precision of the algorithm estimation model is also reduced, and at the moment, the weight coefficient can be increased so as to improve the precision of the algorithm estimation model. For example, if the algorithm estimation model is established for urban road conditions, the weight coefficient may be increased under high-rate conditions such as high-speed conditions. In addition, under the condition of large-rate battery discharge, the time for correcting the algorithm estimation model can be further shortened, and the increase of the weight coefficient is also beneficial to improving the convergence rate.
In addition, when the battery aging degree is high, the accuracy of the algorithm estimation model is also reduced. Therefore, when the battery reaches a certain aging degree, the weight coefficient can be increased to improve the accuracy of the algorithm estimation model.
FIG. 4 is a comparison of the estimated battery state of charge (unadjusted SOC) obtained by the estimation algorithm without the introduction of weighting coefficients, to the accuracy of the estimated battery state of charge (adjusted SOC) in the 85-50% SOC range obtained based on the method of an embodiment of the present invention. FIG. 5 is a comparison of the accuracy of the estimated battery state of charge (unadjusted SOC) obtained by the estimation algorithm without the introduction of weighting factors, in the 85-10% SOC range, with the estimated battery state of charge (adjusted SOC) obtained based on the method of an embodiment of the present invention. As can be seen from fig. 4 and 5, by adding the weight coefficient, the initial SOC error can be converged quickly, thereby improving the accuracy of the end SOC.
In summary, the estimation method 100 of the state of charge of the battery according to the embodiment of the present invention can improve the convergence rate of the algorithm estimation model by introducing the weight coefficient, so as to improve the correction capability of the initial error of the estimated state of charge of the battery.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed, may implement the method 100 for estimating a state of charge of a battery according to embodiments of the present invention.
The embodiment of the present invention further provides a device for estimating a state of charge of a battery, which can be used to implement the method 100 for estimating a state of charge of a battery described above. Referring to fig. 6, fig. 6 shows a schematic block diagram of a battery state of charge estimation apparatus 600 according to an embodiment of the present invention.
As shown in fig. 6, the battery state of charge estimation apparatus 600 includes a memory 610, a processor 620, and a computer program stored in the memory 610 and running on the processor 620, and when the computer program is executed by the processor 620, the estimation method 100 of the battery state of charge as described above can be implemented. The Processor 620 may be implemented by software, hardware, firmware, or a combination thereof, and may use at least one of an electric Circuit, a single or multiple Application Specific Integrated Circuits (ASICs), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor, so that the apparatus may perform a part of or all of the steps of the battery state of charge estimation method 100 in various embodiments of the present Application, or any combination of the steps therein.
The estimation device 600 of the battery state of charge of the embodiment of the invention can improve the convergence rate of the algorithm estimation model by introducing the weight coefficient, thereby improving the correction capability of the initial error of the estimated battery state of charge.
Although the example embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above-described example embodiments are merely illustrative and are not intended to limit the scope of the present application thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the description of exemplary embodiments of the present application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present application should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules according to embodiments of the present application. The present application may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application or the description thereof, and the protection scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of estimating a state of charge of a battery, the method comprising:
establishing a battery model for a battery, and establishing an algorithm estimation model based on the relation between the battery state of charge and the open-circuit voltage of the battery;
obtaining an estimated terminal voltage based on the battery model and the algorithm estimation model, and performing iterative correction on the algorithm estimation model based on a difference value between the estimated terminal voltage and an actual terminal voltage;
in the iterative correction process, an estimated battery state of charge is obtained based on the algorithm estimation model, when the estimated battery state of charge enters a preset range, the difference value is adjusted based on a weight coefficient, iterative correction is performed on the algorithm estimation model based on the adjusted difference value, and the weight coefficient is larger than 1.
2. The method of estimating battery state of charge of claim 1, further comprising:
and determining the weight coefficient in real time according to the working condition, the temperature condition and/or the aging condition of the battery.
3. The method of claim 1, wherein the predetermined range is determined according to a correctable region in a curve relating battery state of charge to open circuit voltage.
4. The method of claim 3, wherein the predetermined range comprises at least one of: 98% -100%, 58% -65% and 0-35%.
5. The method of estimating state of charge of a battery of claim 1, wherein said algorithmic estimation model comprises a kalman filter.
6. The method of estimating battery state of charge of claim 5, wherein said Kalman filter derives the estimated battery state of charge at the current time based on the sum of: an estimated battery state of charge at a previous time, and a product of the difference and a gain;
the introducing of the weight coefficient in the iterative correction comprises: multiplying the product of the difference and the gain by the weight coefficient.
7. The method of estimating state of charge of a battery of claim 1, wherein said battery model comprises an equivalent circuit model.
8. The method of estimating state of charge of a battery of claim 7, wherein deriving an estimated terminal voltage based on said battery model and said algorithmic estimation model comprises:
obtaining an estimated open circuit voltage corresponding to the estimated battery state of charge based on the algorithm estimation model;
and taking the current signal, the voltage signal and the temperature signal of the battery as input signals, inputting the input signals and the estimated open-circuit voltage into the equivalent circuit model after parameter identification, and outputting the estimated terminal voltage.
9. The method of estimating state of charge of a battery according to any of claims 1-8, wherein said battery comprises a lithium iron phosphate battery.
10. A battery state of charge estimation apparatus, comprising a memory and a processor, the memory having stored thereon a computer program for execution by the processor, the computer program, when executed by the processor, performing the battery state of charge estimation method according to any one of claims 1-9.
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