CN116559693A - Battery SOC evaluation method and device, electronic equipment and storage medium - Google Patents
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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
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- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The invention discloses a battery SOC evaluation method, a device, electronic equipment and a storage medium. Comprising the following steps: acquiring open-circuit voltage data and current integral data of a battery, and determining a discharging stage of the battery based on the open-circuit voltage data and the current integral data; and invoking an SOC evaluation strategy corresponding to the discharging stage of the battery, and performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in the battery power supply process. According to the scheme, the discharging stage of the battery is judged through the open-circuit voltage data and the current integral data of the battery, the corresponding SOC evaluation strategy is called according to different discharging stages, the SOC data in the battery power supply process is obtained, the problem of electric quantity evaluation of the battery is solved, the corresponding SOC evaluation strategy is called according to different discharging stages of the battery, the evaluation result of the battery SOC is enabled to be more in accordance with the current running state of the battery, and therefore the accuracy of the evaluation result of the battery SOC is improved.
Description
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to a battery SOC evaluation method, a device, electronic equipment and a storage medium.
Background
With the importance of people on energy crisis and environmental pollution, pure electric vehicles have become a trend and are more and more popular with people. The electric vehicle depends on the energy stored by the power battery, and the electric quantity of the battery determines the endurance mileage of the electric vehicle, so that the electric quantity estimation of the power battery is crucial. The parameter SOC (state of charge) is generally used to represent the battery state of charge.
Currently, since the SOC of a battery is affected by various parameters, such as the use temperature, current multiplying power, and cut-off voltage of the battery, the SOC of the battery is generally evaluated by an open circuit voltage method, an ampere-hour integration method, a charge/discharge end curve correction method, a kalman filter method, or a neural network method.
In the process of realizing the invention, under the condition of carrying out battery evaluation by the prior art, the problems of low evaluation accuracy and large calculation amount caused by current integration errors exist.
Disclosure of Invention
The invention provides a battery SOC evaluation method, a device, electronic equipment and a storage medium, which are used for solving the problems of low evaluation precision and large calculation amount in the battery SOC evaluation process.
According to an aspect of the present invention, there is provided a battery SOC evaluation method including:
acquiring open-circuit voltage data and current integral data of a battery, and determining a discharging stage of the battery based on the open-circuit voltage data and the current integral data;
invoking an SOC evaluation strategy corresponding to a discharge stage where the battery is located, performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in a battery power supply process, and determining the discharge stage where the battery is located based on open circuit voltage data and current integral data, wherein the method comprises the following steps:
determining a current open circuit voltage rate of change based on the open circuit voltage data and the current integration data; the discharge phase in which the battery is placed is determined based on the current open circuit voltage change rate and the change rate threshold. The discharge phase comprises: a first discharge phase and a second discharge phase;
wherein the open circuit voltage change rate of the first discharge phase is smaller than the open circuit voltage change rate of the second discharge phase.
The SOC estimation strategy of the first discharge phase includes: acquiring battery voltage, discharge power, temperature data and current integral data of a battery, and inputting the battery voltage, the discharge power, the temperature data and the current integral data into a pre-trained SOC evaluation model to obtain SOC variation data; based on the previous SOC data and the SOC variation data, current SOC data is obtained.
The second stage SOC estimation strategy includes: acquiring open-circuit voltage data of a battery, calling a pre-calibrated open-circuit voltage-SOC mapping relation, matching the open-circuit voltage data of the battery in the open-circuit voltage-SOC mapping relation, and determining current SOC data corresponding to the open-circuit voltage data.
The method further comprises the steps of: when the battery is detected to be electrified, determining the self-discharge duration of the battery; and under the condition that the self-discharge time length is greater than a preset time length threshold value, correcting the pre-stored battery SOC data to obtain the initial SOC data of the battery at the power-on time.
Correcting the pre-stored battery SOC data to obtain initial SOC data of the battery at the power-on time, wherein the initial SOC data comprises: acquiring self-discharge correction parameters calibrated in advance, and determining correction data based on the self-discharge correction parameters and the self-discharge time length; and acquiring pre-stored battery SOC data, and determining SOC initial data at the power-on time based on the pre-stored battery SOC data and the correction data.
After obtaining the SOC data during battery power, the method further includes: under the condition that the battery voltage of the battery is smaller than the cut-off voltage, setting the SOC data and the current integrator to zero; and under the condition that the battery voltage of the battery is larger than or equal to the cut-off voltage and the SOC data is a preset value, setting the current integrator to zero.
According to another aspect of the present invention, there is provided a battery SOC evaluation apparatus including:
the battery discharging stage determining module is used for acquiring open-circuit voltage data and current integration data of the battery and determining the discharging stage of the battery based on the open-circuit voltage data and the current integration data;
and the battery SOC data determining module is used for calling an SOC evaluation strategy corresponding to the discharging stage of the battery, and carrying out SOC evaluation on the battery based on the called SOC evaluation strategy to obtain the SOC data in the battery power supply process.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the battery SOC estimation method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the battery SOC evaluation method of any of the embodiments of the present invention when executed.
According to the technical scheme, the discharging stage of the battery is judged through the open-circuit voltage data and the current integral data of the battery, the corresponding SOC evaluation strategy is called according to different discharging stages, the SOC data in the battery power supply process is obtained, the problem of electric quantity evaluation of the battery is solved, the corresponding SOC evaluation strategy is called according to different discharging stages of the battery, the evaluation result of the battery SOC is enabled to be more in accordance with the current running state of the battery, and therefore the accuracy of the evaluation result of the battery SOC is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a battery SOC evaluation method according to an embodiment of the present invention;
fig. 2 is a flowchart of a battery SOC evaluation method according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a battery SOC estimation segmentation calculation method applicable to an embodiment of the present invention;
fig. 4 is a flowchart of a battery SOC evaluation method according to a third embodiment of the present invention;
FIG. 5 is a flowchart of a preferred battery SOC estimation method provided by the present invention;
FIG. 6 is a schematic diagram of a neural network model to which embodiments of the present invention are applicable;
fig. 7 is a schematic structural diagram of a battery SOC estimation apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device implementing a battery SOC estimation method of an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the prior art, methods for evaluating the SOC of the battery employ an open circuit voltage method, an ampere-hour integration method, a charge-discharge end curve correction method, a kalman filter method, a neural network method, and the like. The open-circuit voltage method refers to that the SOC state can be obtained by measuring a relation curve of the open-circuit voltage of the battery and the SOC of the battery in advance and looking up a table according to the measured open-circuit voltage of the battery in the application process, but the method can accurately estimate the battery with obvious change of the corresponding battery voltage and can not be used for the lithium iron phosphate battery with a voltage platform; the ampere-hour integrating method is used for integrating current in the using process of the battery so as to obtain the electric quantity discharged/flushed by the battery and estimating the SOC state of the battery once, and the error of current integration can be accumulated to influence the SOC estimation precision and the influence of temperature change on SOC estimation can not be estimated; the charge-discharge end curve correction method is to estimate the SOC by utilizing the sharp change of the voltage of the charge-discharge end of the battery, so that the full or insufficient state of the battery can be accurately judged, but the state cannot be judged for the intermediate state; the Kalman filtering method is an algorithm for optimally estimating the system state by utilizing a linear system state equation and inputting and outputting observation data through a system, but is affected by the accuracy of a vehicle-mounted sensor, so that the convergence speed and the accuracy of the Kalman filtering algorithm are poor, and the problem of large calculation amount exists; the neural network method is a method which can train a set neural network model through a large amount of experimental data under the condition of not providing an accurate mathematical formula, so that the aim of predicting the battery SOC is fulfilled, but the problems of higher requirements on setting of input quantity and output quantity and higher dependence on a training method and training data exist. Therefore, the scheme adopts the segmentation method to evaluate the SOC of the battery, namely, corresponding SOC evaluation strategies are set for different discharging stages of the battery, so that the problem of high calculation amount requirement of the whole process adopting the neural network for calculation can be avoided, errors caused by calculating the SOC by referring to the ampere-hour integration method can be reduced, and the accuracy of estimating the SOC of the battery is effectively improved.
Example 1
Fig. 1 is a flowchart of a battery SOC estimation method according to an embodiment of the present invention, where the method may be applied to a case of estimating a battery SOC, and the method may be performed by a battery SOC estimation apparatus, which may be implemented in hardware and/or software, and the battery SOC estimation apparatus may be configured in an electronic device such as a vehicle controller, a computer, or the like. As shown in fig. 1, the method includes:
s110, acquiring open-circuit voltage data and current integral data of the battery, and determining a discharging stage of the battery based on the open-circuit voltage data and the current integral data.
The open circuit voltage data may be specifically understood as data obtained by the battery in an open circuit state, and may include, but is not limited to, an open circuit voltage of the battery, that is, a difference between an electrode potential of a positive electrode and an electrode potential of a negative electrode of the battery when the battery is disconnected, a voltage variation amount in a preset period, and the like. The open circuit voltage data may be detected and acquired by a voltage detection device. The current integration data is specifically understood to be data obtained by integrating the charge/discharge current of the battery, and can be calculated by an ampere-hour integration method. The discharging stage may be specifically understood as a result of dividing the discharging process of the battery according to factors such as a discharging duration of the battery, an open circuit voltage, current integral data, and the like, for example, the discharging stage may be set to a first discharging stage, a second discharging stage, and the like, and a dividing rule may be preset according to a discharging condition of the battery, and for example, a discharging stage may be determined according to a range in which the discharging duration is located, and different discharging stages correspond to different discharging duration ranges. For example, the discharge phase may be determined according to a rule of dividing the discharge phase by comprehensively considering factors such as a power-down time period, an open circuit voltage, current integration data, etc., for example, the discharge phase may be divided according to a change rate of the open circuit voltage, for example, the discharge phase may be divided into a stationary discharge phase (for example, a first discharge phase) and a non-stationary discharge phase (for example, a second discharge phase), and accordingly, the associated discharge phase may be determined according to a determination result of the change rate of the open circuit voltage and a threshold value.
Specifically, the open-circuit voltage of the battery can be detected through the voltage detection equipment, open-circuit voltage data of the battery are determined, the current in the using process of the battery is integrated through an ampere-hour integration method, the charge/discharge electric quantity of the battery is obtained, and the current discharge stage of the battery is judged according to the obtained open-circuit voltage data and current integration data of the battery.
In this embodiment, the current discharge stage of the battery is determined according to the open circuit voltage data and the current integral data of the battery, so that the matching SOC evaluation strategy is invoked according to the determined discharge stage, and the accuracy of the battery SOC evaluation is improved.
S120, invoking an SOC evaluation strategy corresponding to the discharging stage of the battery, and performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in the battery power supply process.
The SOC estimation policy may be specifically understood as an estimation policy set in advance according to characteristics of a discharge phase of the battery, where different discharge phases correspond to one SOC estimation policy, respectively. SOC data may be understood as the SOC value of the battery, which may be obtained by the ratio of the open circuit voltage of the battery to the total charge capacity of the battery, typically expressed in percent.
Specifically, a discharge stage of the battery is determined according to the obtained open-circuit voltage data and the current integral data, and an SOC evaluation strategy corresponding to the discharge stage of the current battery is called to obtain SOC data in the battery power supply process. For example, corresponding SOC estimation strategies may be set in advance for different discharge phases, and after the discharge phase in which the battery is located is determined, the corresponding SOC estimation strategies are invoked.
Further, the method further comprises: when the battery is detected to be electrified, determining the self-discharge duration of the battery; and under the condition that the self-discharge time length is greater than a preset time length threshold value, correcting the pre-stored battery SOC data to obtain the initial SOC data of the battery at the power-on time.
The self-discharge time period is specifically understood to be the time period from the start of the battery power-down time to the time when the battery power-up time is tested. The detected battery power-on time and power-off time can be transmitted to equipment such as a control console or a server through the detection equipment for recording and storage, and the follow-up test or the check of the equipment can be conveniently checked.
Specifically, the self-discharge condition of the current battery is determined by setting a preset duration threshold and comparing and judging the self-discharge duration of the battery, if the self-discharge duration of the battery is longer than the preset duration threshold, the battery is judged to be too long in standing time, the self-discharge condition of the battery cannot be ignored when determining the SOC evaluation strategy, and pre-stored battery SOC data needs to be corrected, so that initial SOC data of the battery at the power-on moment is obtained.
Optionally, correcting pre-stored battery SOC data to obtain initial SOC data of the battery at the time of power-up, including: acquiring self-discharge correction parameters calibrated in advance, and determining correction data based on the self-discharge correction parameters and the self-discharge time length; and acquiring pre-stored battery SOC data, and determining SOC initial data at the power-on time based on the pre-stored battery SOC data and the correction data.
The self-discharge is specifically understood to be the charge retention capacity of a battery, which refers to the capacity of the battery to store the amount of electricity stored under certain environmental conditions in an open state, that is, the amount of electricity is still reduced without a galvanic reaction when the battery is open, mainly due to the self-discharge of the battery. The discharge time and the discharge correction parameters of the battery can be calibrated in advance through experimental data, and the battery can be stored in a mode of a relation table of the self-discharge correction parameters and the self-discharge duration.
Specifically, the self-discharge time length of the battery is firstly determined, a table of the relationship between the self-discharge correction parameters and the self-discharge time length calibrated in advance is checked through a table look-up method, data matched with the self-discharge correction parameters and the self-discharge time length of the battery are obtained, and the discharge correction data of the battery are determined. The value of the discharge correction data is a negative value. The pre-stored SOC value of the battery and the discharge correction data of the battery are obtained, and the pre-stored SOC value and the discharge correction data of the battery can be added to obtain the SOC value, namely the initial SOC data of the battery at the power-on time.
In this embodiment, whether the pre-stored SOC data of the battery needs to be corrected is determined according to the self-discharge time length of the battery, and when the correction is determined to be needed, the correction is performed according to the pre-calibrated battery correction data, so that the obtained SOC initial data of the battery at the power-on time is more accurate, and the accuracy of the battery SOC estimation is further improved.
According to the technical scheme, the discharging stage of the battery is judged through the open-circuit voltage data and the current integral data of the battery, the corresponding SOC evaluation strategy is called according to different discharging stages, the SOC data in the battery power supply process is obtained, the problem of large electric quantity evaluation error of the battery is solved, the corresponding SOC evaluation strategy is called according to different discharging stages of the battery, the evaluation result of the battery SOC is enabled to be more in accordance with the current running state of the battery, and therefore the accuracy of the evaluation result of the battery SOC is improved.
Example two
FIG. 2 is a flow chart of a battery SOC estimation method according to a second embodiment of the present invention, wherein the method according to the above embodiment is further optimized, and optionally, the current open circuit voltage change rate is determined based on open circuit voltage data and current integration data; the discharge phase in which the battery is placed is determined based on the current open circuit voltage change rate and the change rate threshold. As shown in fig. 2, the method includes:
And S210, acquiring open circuit voltage data and current integration data of the battery.
S220, determining the current open circuit voltage change rate based on the open circuit voltage data and the current integral data.
The open-circuit voltage change rate refers to the ratio of the open-circuit voltage difference value of the voltage to the ampere-hour of the current integration in a preset time period.
Specifically, the preset time period can be set as the time length between the discharging time of the battery and the re-electrifying time of the battery, the difference value of the open-circuit voltage in the time period is calculated, the current integral ampere-hour number is calculated through an ampere-hour integration method, and then the current integral ampere-hour number is divided by the difference value of the open-circuit voltage, and the calculated result is used as the current open-circuit voltage change rate of the battery.
S230, determining the discharging stage of the battery based on the current open circuit voltage change rate and the change rate threshold.
Specifically, the change rate threshold value can be set according to experimental data, and the discharge stage of the battery is determined according to the current open circuit voltage change rate and the change rate threshold value, namely, the discharge stage of the battery is judged according to the current open circuit voltage change rate and the change rate threshold value. For example, if the current open circuit voltage change rate is greater than the change rate threshold, it indicates that the battery is not in the voltage plateau, otherwise, the battery is in the voltage plateau.
Optionally, the discharging phase comprises: a first discharge phase and a second discharge phase; wherein the open circuit voltage change rate of the first discharge phase is smaller than the open circuit voltage change rate of the second discharge phase.
Specifically, the first discharging stage may be understood as a stage in which the battery is in a voltage plateau stage, that is, the current open circuit voltage change rate is less than or equal to the change rate threshold value, and the second discharging stage may be understood as a stage in which the battery is not in a voltage plateau stage, that is, the current open circuit voltage change rate is greater than the change rate threshold value, and it may be understood that the open circuit voltage change rate in the first discharging stage is less than the change rate in the second discharging stage.
Optionally, the SOC estimation strategy of the first discharge phase includes: acquiring battery voltage, discharge power, temperature data and current integral data of a battery, and inputting the battery voltage, the discharge power, the temperature data and the current integral data into a pre-trained SOC evaluation model to obtain SOC variation data; based on the previous SOC data and the SOC variation data, current SOC data is obtained.
The SOC estimation model may be specifically understood as a model for estimating the SOC of the battery by using a neural network method, and the SOC estimation model may be obtained in advance through a test. For example, a BP neural network may be adopted, four parameters may be set in the input layer, the input parameters may include battery voltage, discharge power, temperature data, current integral data, and the like, and only one parameter of the output layer, that is, the variation of the SOC value, is represented by Δsoc.
Specifically, when the battery is in the first discharging stage, namely the battery is in the voltage platform stage, the battery voltage, the discharging power, the average temperature and the current integral are obtained, the data are used as input parameters of an SOC estimation model, and the SOC estimation model is used for processing the data to output the SOC variation of the battery. Further, the SOC data of the battery stored in the previous time and the delta SOC of the battery are added, and the obtained result is the current SOC data.
In this embodiment, a neural network method is adopted to estimate the SOC of the battery, and the input parameters include battery voltage, discharge power, temperature data, current integral data, and the like, and the influence of the temperature and the discharge power on the SOC of the battery is considered, and the current integral data is introduced to estimate the charge and discharge electric quantity, so that the accuracy of the SOC estimation model on SOC estimation is improved.
Optionally, the SOC estimation policy of the second stage includes: acquiring open-circuit voltage data of a battery, calling a pre-calibrated open-circuit voltage-SOC mapping relation, matching the open-circuit voltage data of the battery in the open-circuit voltage-SOC mapping relation, and determining current SOC data corresponding to the open-circuit voltage data.
Specifically, when the battery is in the second discharging stage, namely the battery is not in the voltage platform stage, open-circuit voltage data of the battery are obtained, a pre-calibrated open-circuit voltage-SOC mapping relation is checked through a table lookup method, calibration data matched with the open-circuit voltage data of the battery are determined, and current SOC data corresponding to the open-circuit voltage data are obtained.
Exemplary, a schematic diagram of a battery SOC estimation segmentation calculation method is shown in fig. 3, where the ordinate represents the open-circuit voltage of the battery, the abscissa may represent the SOC value of the battery, and the discharging duration of the battery, and the graph is calibrated according to the change rate of the open-circuit voltage of the battery during the discharging process. Under the condition that the battery is not in the voltage platform stage, the voltage change is obvious, so that an SOC evaluation strategy of the second discharging stage is adopted, as shown in the graph, the curve stage corresponding to the OCV table lookup method can be seen from the graph, according to the graph 3, the two places where the slope change of the curve is obvious are shown, namely, the number of the second discharging stages comprises two, so that the number of the second discharging stages can be understood to be at least one; in the case that the battery is in the voltage platform stage, the voltage change is not obvious, so that the SOC evaluation strategy of the first discharging stage is adopted, such as a curve stage corresponding to the neural network method shown in the figure.
In this embodiment, since the battery has the characteristics of the discharging platform, the battery SOC is evaluated by adopting the segmentation method, that is, corresponding SOC evaluation strategies are set for different discharging stages of the battery, so that the high calculation amount requirement of the whole process adopting the neural network to calculate can be avoided, errors caused by calculating the SOC by referencing the ampere-hour integration method can be reduced, and the accuracy of estimating the battery SOC is effectively improved.
S240, invoking an SOC evaluation strategy corresponding to the discharging stage of the battery, and performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in the battery power supply process.
According to the technical scheme, the discharging stage of the battery is determined based on the current open-circuit voltage change rate and the change rate threshold, corresponding SOC estimation strategies are selected according to different discharging stages, the segmentation method is adopted for estimating the SOC of the battery, the requirement of high calculation amount calculated by a neural network in the whole process is avoided, errors caused by calculating the SOC by referencing an ampere-hour integration method can be reduced, and the accuracy of estimating the SOC of the battery is effectively improved.
Example III
Fig. 4 is a flowchart of a battery SOC estimation method according to a third embodiment of the present invention, where the method according to the above embodiment is further optimized, and optionally, in a case where the battery voltage of the battery is less than the cutoff voltage, the SOC data and the current integrator are set to zero; and under the condition that the battery voltage of the battery is larger than or equal to the cut-off voltage and the SOC data is a preset value, setting the current integrator to zero. As shown in fig. 4, the method includes:
and S310, acquiring open-circuit voltage data and current integral data of the battery, and determining the discharging stage of the battery based on the open-circuit voltage data and the current integral data.
S320, invoking an SOC evaluation strategy corresponding to the discharging stage of the battery, and performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in the battery power supply process.
And S330, setting the SOC data and the current integrator to be zero when the battery voltage of the battery is smaller than the cut-off voltage.
Wherein the cut-off voltage refers to the discharge end voltage of the battery, and is a voltage value obtained by stopping discharge after the voltage is reduced to a certain degree in the discharging process of the battery, and V can be adopted min The representation may be obtained by experimental test methods, may also be obtained based on analog calculation methods, and is not limited herein.
In addition, since the SOC estimation error becomes large at the end of the discharge of the battery, the obtained SOC value needs to be corrected according to the battery voltage after the current SOC estimation is completed.
Specifically, the battery voltage and the cut-off voltage of the obtained battery are compared, when the battery voltage of the battery is smaller than the cut-off voltage, the electric quantity of the battery is used up, the SOC value of the battery can be set to 0, and meanwhile, the value of the current integrator is set to 0.
S340, setting the current integrator to zero when the battery voltage of the battery is greater than or equal to the cut-off voltage and the SOC data is a preset value.
The preset value may be understood as a value for determining the current state of charge setting of the battery, for example, may be set to 100% or 0, and when the preset value is 100%, it indicates that the battery is full, and when the preset value is 0, it indicates that the battery is empty.
Specifically, preset values can be preset to be 100% and 0, and if the battery voltage of the battery is greater than or equal to the cut-off voltage, whether the SOC is equal to the preset value is judged, if the SOC is equal to the preset value, the current integrator needs to be set to zero, and accordingly, the accumulated error of the current integration is cleared. Further, if the battery SOC is not equal to the preset value, the SOC value at that time is directly recorded.
In a preferred embodiment, as shown in fig. 5, the flow of the battery SOC evaluation method is to determine whether the self-discharge duration of the battery is greater than t, where t represents a preset duration threshold, and when the self-discharge duration of the battery is greater than t, the sum of the SOC value of the battery recorded last time and the self-discharge correction value is taken as the initial SOC value of the battery, and when the self-discharge duration of the battery is not greater than t, the SOC value of the battery recorded last time is taken as the initial SOC value of the battery. Further judging whether delta OCV/delta ah is larger than a, wherein delta OCV represents a difference value of open circuit voltage, delta ah represents a current integral ampere hour number, a represents a preset change rate threshold value, and if delta OCV/delta ah is larger than a, determining a battery SOC value through a table look-up method, namely checking an OCV-SOC curve to obtain an SOC value matched with open circuit voltage data as a current SOC value; if Δocv/Δah is not greater than a, performing SOC estimation by using an SOC estimation model including a neural network method, where the neural network model is shown in fig. 6, and uses the battery voltage, the average power, the average temperature, and the current integral ampere hour as input parameters of the neural network model, that is, input parameters of the SOC estimation model, and after model processing, outputting an SOC variation value Δsoc, where the current SOC value is a sum of the initial SOC value and Δsoc. Further, judging the magnitude relation between the battery voltage and the discharge cut-off voltage, correcting the SOC value, and setting the SOC value to 0 if the battery voltage is smaller than or equal to the discharge cut-off voltage; otherwise, judging whether the SOC value is equal to 100% or 0, wherein 100% or 0 is a preset value, if the SOC value is equal to 100% or 0, setting the current integrator to 0, and if the SOC value does not meet the condition of equal to 100% or 0, recording the current SOC value, and ending the current battery SOC evaluation process.
According to the technical scheme, through judging the magnitude relation between the battery voltage and the cut-off voltage of the battery, the SOC value is further corrected according to the battery voltage, the error of SOC estimation is reduced, and the accuracy of the SOC estimation is improved.
Example IV
Fig. 7 is a schematic structural diagram of a battery SOC estimation device according to a fourth embodiment of the present invention.
As shown in fig. 7, the apparatus includes:
a battery discharge phase determining module 710, configured to obtain open circuit voltage data and current integration data of the battery, and determine a discharge phase in which the battery is located based on the open circuit voltage data and the current integration data;
the battery SOC data determining module 720 is configured to invoke an SOC evaluation policy corresponding to a discharging stage where the battery is located, and perform SOC evaluation on the battery based on the invoked SOC evaluation policy, so as to obtain SOC data in a battery power supply process.
Optionally, the battery discharge stage determining module 710 further includes:
an open-circuit voltage change rate determination unit 711 specifically configured to determine a current open-circuit voltage change rate based on open-circuit voltage data and current integration data;
the discharge phase determining unit 712 is specifically configured to determine a discharge phase in which the battery is located based on the current open circuit voltage change rate and the change rate threshold.
Optionally, the discharge phase determining unit 712 is specifically configured to:
a first discharge phase and a second discharge phase; wherein the open circuit voltage change rate of the first discharge phase is smaller than the open circuit voltage change rate of the second discharge phase.
The SOC estimation strategy of the first discharge phase includes:
acquiring battery voltage, discharge power, temperature data and current integral data of a battery, and inputting the battery voltage, the discharge power, the temperature data and the current integral data into a pre-trained SOC evaluation model to obtain SOC variation data;
based on the previous SOC data and the SOC variation data, current SOC data is obtained.
The second stage SOC estimation strategy includes:
acquiring open-circuit voltage data of a battery, calling a pre-calibrated open-circuit voltage-SOC mapping relation, matching the open-circuit voltage data of the battery in the open-circuit voltage-SOC mapping relation, and determining current SOC data corresponding to the open-circuit voltage data.
Optionally, the method further comprises:
when the battery is detected to be electrified, determining the self-discharge duration of the battery;
and under the condition that the self-discharge time length is greater than a preset time length threshold value, correcting the pre-stored battery SOC data to obtain the initial SOC data of the battery at the power-on time.
Optionally, after obtaining SOC data in the battery power supply process, the method further includes:
under the condition that the battery voltage of the battery is smaller than the cut-off voltage, setting the SOC data and the current integrator to zero;
and under the condition that the battery voltage of the battery is larger than or equal to the cut-off voltage and the SOC data is a preset value, setting the current integrator to zero.
The battery SOC evaluation device provided by the embodiment of the invention can execute the battery SOC evaluation method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 8 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 8, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as the battery SOC evaluation method.
In some embodiments, the battery SOC evaluation method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into the RAM13 and executed by the processor 11, one or more steps of the battery SOC estimation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the battery SOC estimation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the battery SOC estimation method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Example six
The sixth embodiment of the present invention also provides a computer readable storage medium storing computer instructions for causing a processor to execute a battery SOC evaluation method, the method including:
acquiring open-circuit voltage data and current integral data of a battery, and determining a discharging stage of the battery based on the open-circuit voltage data and the current integral data;
and invoking an SOC evaluation strategy corresponding to the discharging stage of the battery, and performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in the battery power supply process.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (11)
1. A battery SOC evaluation method, characterized by comprising:
acquiring open-circuit voltage data and current integral data of a battery, and determining a discharge stage of the battery based on the open-circuit voltage data and the current integral data;
and invoking an SOC evaluation strategy corresponding to the discharging stage of the battery, and performing SOC evaluation on the battery based on the invoked SOC evaluation strategy to obtain SOC data in the battery power supply process.
2. The method of claim 1, wherein the determining a discharge phase in which the battery is placed based on the open circuit voltage data and the current integration data comprises:
determining a current open circuit voltage rate of change based on the open circuit voltage data and the current integration data;
and determining the discharging stage of the battery based on the current open circuit voltage change rate and the change rate threshold.
3. The method of claim 2, wherein the discharge phase comprises: a first discharge phase and a second discharge phase;
wherein the open circuit voltage change rate of the first discharge phase is smaller than the open circuit voltage change rate of the second discharge phase.
4. The method of claim 3, wherein the SOC estimation strategy for the first discharge phase comprises:
Acquiring battery voltage, discharge power, temperature data and current integral data of the battery, and inputting the battery voltage, the discharge power, the temperature data and the current integral data into a pre-trained SOC evaluation model to obtain SOC variation data;
and obtaining current SOC data based on the previous SOC data and the SOC variation data.
5. The method of claim 3, wherein the second stage SOC estimation strategy comprises:
acquiring open-circuit voltage data of a battery, calling a pre-calibrated open-circuit voltage-SOC mapping relation, matching the open-circuit voltage data of the battery in the open-circuit voltage-SOC mapping relation, and determining current SOC data corresponding to the open-circuit voltage data.
6. The method according to any one of claims 1-5, further comprising:
when the battery is detected to be electrified, determining the self-discharge duration of the battery;
and correcting pre-stored battery SOC data under the condition that the self-discharge time length is larger than a preset time length threshold value to obtain the initial SOC data of the battery at the power-on time.
7. The method of claim 6, wherein correcting the pre-stored battery SOC data to obtain initial SOC data for the battery at a power-up time comprises:
Acquiring self-discharge correction parameters calibrated in advance, and determining correction data based on the self-discharge correction parameters and the self-discharge duration;
and acquiring the pre-stored battery SOC data, and determining the initial SOC data at the power-on moment based on the pre-stored battery SOC data and the correction data.
8. The method of claim 1, further comprising, after obtaining SOC data during battery power, the step of:
setting the SOC data and the current integrator to zero when the battery voltage of the battery is less than a cutoff voltage;
and under the condition that the battery voltage of the battery is larger than or equal to the cut-off voltage and the SOC data is a preset value, setting the current integrator to zero.
9. A battery SOC estimation apparatus, characterized by comprising:
the battery discharging stage determining module is used for acquiring open-circuit voltage data and current integration data of the battery and determining the discharging stage of the battery based on the open-circuit voltage data and the current integration data;
and the battery SOC data determining module is used for calling an SOC evaluation strategy corresponding to the discharging stage of the battery, and carrying out SOC evaluation on the battery based on the called SOC evaluation strategy to obtain the SOC data in the battery power supply process.
10. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the battery SOC estimation method of any of claims 1-8.
11. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the battery SOC estimation method of any of claims 1-8 when executed.
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