CN116840699B - Battery health state estimation method and device, electronic equipment and medium - Google Patents
Battery health state estimation method and device, electronic equipment and medium Download PDFInfo
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Abstract
The battery state of health estimation method, the device, the electronic equipment and the medium provided by the embodiment of the invention have the following beneficial effects: acquiring sampling voltage difference values corresponding to a plurality of charge states; under the condition of a plurality of battery health states, calculating corresponding simulation voltage difference values according to the plurality of charge states; and calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result. According to the invention, the voltage difference value is subjected to model simulation, the battery health state is estimated through error numerical calculation, and the calculated value with the smallest error is used as an estimation result, so that the rapid model simulation is realized, the resource occupation is small, and the estimation efficiency and accuracy are effectively improved.
Description
Technical Field
The present invention relates to the field of computers, and in particular, to a method, an apparatus, an electronic device, and a medium for estimating a battery state of health.
Background
SOC (state of charge) and SOH (state of health) of a battery are two very important states in a battery management system, and determine the current remaining state of charge and the state of health of the battery, which are mutually coupled and mutually affected. When estimating the SOC, the SOH with high precision is required to determine the maximum available capacity of the battery, so that the accuracy of the maximum available capacity is ensured. Accurate parameter characteristics need to be obtained under different states of SOC when estimating SOH. In addition, after the capacity is attenuated to a certain degree, the impedance becomes larger, the pressure drop becomes larger, the discharge capacity is weaker, the temperature is quickly increased, the internal lithium crystal branches are accumulated to a certain degree, and the risk of thermal runaway is caused, so that the accurate SOH estimation can effectively control the thermal runaway risk.
At present, SOH can be represented by characteristics such as impedance, pressure drop change, temperature change and the like, but a full life cycle calibration experiment is required to be performed in a laboratory to obtain a change rule of a characterization parameter, so that an empirical model is generated. The mode, the consumption time and the labor cost of the mode face a non-uniform complex system, a full-multiplying power working condition, a full-temperature change range and the like in practical application. SOH has characteristics of instant decay, strong time variation, nonlinearity and the like, and accurate estimation is difficult to realize.
Therefore, how to improve the estimation efficiency and accuracy of the battery state of health is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In order to overcome the defects of low efficiency and poor precision of the traditional battery state of health estimation method, the invention provides a battery state of health estimation method, a device, electronic equipment and a medium.
In order to achieve the above object, according to a first aspect of the present invention, an embodiment of the present invention provides a battery state of health estimation method, including the steps of:
obtaining sampling voltage differences corresponding to a plurality of states of charge includes: acquiring an initial value meeting a battery standing condition; when the battery meets the temperature condition and is charged with constant current, calculating the state of charge according to the initial value; when the battery constant current charge passes through the preset polarization time and the state of charge is larger than a preset state of charge threshold value, acquiring a sampling voltage corresponding to each state of charge, and taking the difference of adjacent sampling voltages as a sampling voltage difference value of the corresponding state of charge;
under the condition of a plurality of battery health states, calculating corresponding simulation voltage difference values according to the plurality of charge states, wherein the simulation voltage difference values comprise: calculating reference values of all states of charge according to the state of health of the battery; acquiring corresponding battery open-circuit voltage, ohmic impedance and polarization impedance from a calibrated battery parameter table according to the reference value; according to the open-circuit voltage of the battery, the ohmic impedance and the polarization impedance, calculating simulation voltages corresponding to all reference values; calculating simulation voltage differences of adjacent reference values to obtain simulation voltage difference values;
and calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result.
Alternatively, the calculation formula of the state of charge is as follows:
SOC(K)=SOC(K-1)+I/(CN×3600)，
wherein CN represents the rated capacity of the battery, I represents the constant current charging current, K is a natural number equal to or greater than 1, and SOC (0) represents the initial value.
Optionally, the preset state of charge threshold is between 30% and 60%, and the preset polarization time is 2 to 5 times of the battery equivalent circuit time constant.
Optionally, the reference value of the state of charge is calculated as follows:
wherein i=1, 2 … m, m is the number of simulation points; SOCstep (i) is a reference value for state of charge, SOC (i) represents one of the plurality of states of charge, SOCINIAL is the initial value, and SOHstep represents one of the plurality of battery states of health.
Optionally, the plurality of battery states of health comprises a number between 80% and 100% and increasing in steps of 1%.
Optionally, the plurality of battery states of health includes a value between 80% and 100% that varies in a dynamic step size that is positively correlated with a rate of error change corresponding to a last battery state of health.
Optionally, the calculating the error of each of the sampled voltage difference and the simulated voltage difference includes:
calculating the difference between each sampling voltage difference value and the simulation voltage difference value;
and summing the absolute values of the differences to obtain the error.
According to a second aspect of the present invention, an embodiment of the present invention further provides a battery state of health estimation apparatus, including:
the sampling module is used for acquiring sampling voltage difference values corresponding to a plurality of charge states;
the simulation module is used for calculating corresponding simulation voltage difference values according to the multiple states of charge under the condition of multiple battery health states;
and the result module is used for calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result.
According to a third aspect of the present invention, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the battery state of health estimation method as in any of the above embodiments when the computer program is executed.
According to a fourth aspect of the present invention, an embodiment of the present invention further provides a storage medium having stored therein at least one instruction, at least one program, a set of codes or a set of instructions, the at least one instruction, the at least one program, the set of codes or the set of instructions being loaded and executed by a processor to implement the steps of the battery state of health estimation method as in any of the above embodiments.
As described above, the method, the device, the electronic device and the medium for estimating the battery state of health provided by the embodiment of the invention have the following beneficial effects: acquiring sampling voltage difference values corresponding to a plurality of charge states; under the condition of a plurality of battery health states, calculating corresponding simulation voltage difference values according to the plurality of charge states; and calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result. According to the invention, the voltage difference value is subjected to model simulation, the battery health state is estimated through error numerical calculation, and the calculated value with the smallest error is used as an estimation result, so that the rapid model simulation is realized, the resource occupation is small, and the estimation efficiency and accuracy are effectively improved.
Drawings
Fig. 1 is a flowchart of a battery state of health estimation method according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for calculating a sampling voltage difference according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for calculating a simulated voltage difference according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of an error calculation method according to an embodiment of the present invention;
FIG. 5 illustrates a simulated effect diagram of a plurality of SOHs;
fig. 6 illustrates a voltage characteristic diagram of soh=0.89;
fig. 7 illustrates a simulation effect diagram of soh=0.89;
fig. 8 is a schematic structural diagram of a battery state of health estimation device according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device for performing a battery state of health estimation method according to an embodiment of the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and 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.
Please refer to fig. 1 to 9. It should be noted that, the illustrations provided in the present embodiment merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings rather than the number, shape and size of the components in actual implementation, and the form, number and proportion of each component in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
Referring to fig. 1, a flow chart of a battery state of health estimation method according to an embodiment of the present invention is shown in fig. 1, where the embodiment of the present invention shows a flow chart of the battery state of health estimation method.
Step S101: and acquiring sampling voltage difference values corresponding to the charge states.
The state of charge SOC being electricalThe ratio of the remaining capacity of the pool after a period of use or prolonged rest to the capacity of its fully charged state is often expressed as a percentage. The value range is 0-1, and the battery is completely discharged when the SOC=0 and completely full when the SOC=1. In the embodiment of the invention, the battery terminal voltage under a plurality of SOCs is collected to be V Sample Representing by calculating V of adjacent SOCs Sample Thereby obtaining a corresponding sampling voltage difference DeltaV Sample 。
In specific implementation, referring to fig. 2, a flow chart of a method for calculating a sampling voltage difference according to an embodiment of the present invention is shown in fig. 2, and the method includes the following steps.
Step S1011: and obtaining an initial value meeting the battery standing condition.
And under the condition that the battery meets the standing condition, obtaining the terminal voltage of the battery, and obtaining the initial value of the state of charge under the standing condition through the corresponding relation between the open circuit voltage and the state of charge. In a specific embodiment, under the condition of standing the battery, the terminal voltage of the battery is 3.554V, and the initial value SOC of the state of charge is obtained by inquiring the corresponding relation between the open circuit voltage and the state of charge Initial 0.0258.
The correspondence between open circuit voltage and state of charge may be characterized off-line by laboratory calibrated battery parameters, which in an exemplary embodiment are shown below:
wherein, SOC is state of charge; OCV is open circuit voltage, unit is V; r0 is ohmic impedance, and the unit is omega; r1 is polarization impedance, and the unit is omega; c1 is the polarization capacitance in F.
The initial value of the state of charge in the above embodiment is 0.0258, and is obtained by calculating the linear difference of the open-circuit voltage 3.554V according to the above calibration battery parameters, and the specific calculation process is not described herein.
Step S1012: and when the battery meets the temperature condition and is charged with constant current, calculating the charge state according to the initial value.
When the problem condition of the battery is met and the battery is in a constant current charging state, other charge states are further calculated according to the charge state initial value obtained in the steps.
Since the low temperature resistance is large and the capacity fade is serious, the temperature condition is set to be more than 15 ℃ in the practical implementation. In an exemplary embodiment, the temperature condition may be 25 ℃. The battery is in a constant current state of charge, constant current I is 2.453a±10mA in an exemplary embodiment.
Other states of charge are further calculated by ampere-hour integration, as follows:
SOC(K)=SOC(K-1)+I/(CN×3600)，
wherein CN is rated capacity of the battery, and the unit is AH; i is constant current charging current; k is a natural number which is more than or equal to 1 and is used for identifying the number of SOC; the current calculation was 1 s/time, accounting to hour usage 3600.
Thus, according to the above formula, an arbitrary SOC (K) can be calculated by substituting the initial value of the state of charge.
Step S1013: when the battery constant current charging is performed for a preset polarization time and the state of charge is larger than a preset state of charge threshold value, sampling voltage corresponding to each state of charge is obtained, and the difference of adjacent sampling voltages is used as a sampling voltage difference value of the corresponding state of charge.
When the sampling voltage is obtained, the battery sequence enters a constant current charging state and exceeds a preset polarization time which is 2 to 5 times of the time constant of the equivalent circuit of the battery, and the time constant is higher than the preset polarization time to ensure that the battery finishes polarizationWherein R1 is the polarization impedance, and C1 is the polarization capacitance. In the embodiment of the present invention, the preset polarization time is set as followsSpecifically, the preset planning time is obtained by inquiring the calibrated battery parameters=5×0.054×1354≈305s。
To further ensure the estimation accuracy, the battery needs to be in a high SOC segment, so the preset state of charge threshold is between 30% and 60%, and the number of sampled data is greater than 30. In an exemplary embodiment, the preset state of charge threshold is 30%, and the number of sampled data is 37, i.e. after the battery reaches a temperature condition, is in a constant current state, and polarization is complete, the SOC>Using a variable to save the voltage value at the current time when 30% is changed by 1% of rated capacityAs a history value at the next time. And taking a difference between the sampling voltage of the current sensor and the last sampling voltage value to obtain a sampling voltage difference value:
preservingSimultaneously preserve。
Specifically, the 37 SOCs are represented by an array, and the corresponding sampled voltage differences are as follows:
SOC[37]=
where SOC (1) =0.300125083296173, SOC (2) = 0.310114518464181, and so on.
[37]=
Wherein,(1) = 0.004000000000000, which is the difference between the soc=30% corresponding to the sampled voltage and the soc=29% corresponding to the sampled voltage;(2) = 0.00300000000000011, is the difference between the soc=31% and soc=30% of the corresponding sample voltage, and so on.
Step S102: under the condition of a plurality of battery health states, corresponding simulation voltage difference values are calculated according to the plurality of charge states.
And further calculating a simulation voltage difference value corresponding to each battery health state through a simulation model. Referring to fig. 3, a flow chart of a method for calculating a simulated voltage difference according to an embodiment of the present invention is shown, and the embodiment of the present invention shows a flow chart of the calculation of the simulated voltage difference.
Step S1021: and calculating reference values of the charge states according to the state of health of the battery.
In particular implementations, the battery state of health may include values between 80% and 100% that increment in 1% steps, e.g., from 80%, 81% up to 100%, usingAnd (3) representing.
In an exemplary embodiment, whenWhen=0.8, the reference value of the state of charge in the state of health of the battery is calculated as follows:
wherein i=1, 2 … m, m is the number of simulation points, and m is 37 in the embodiment of the invention. SOC (State of Charge) step The SOC is the reference value of the state of charge, and is the state of charge corresponding to the sampling in the step Initial SOH as initial value of state of charge step For battery health, the increment is from 0.8 to 1 in 0.01 steps in the embodiment of the invention.
By substituting a known SOC (1) =0.300125083296173, SOC Initial =0.0258 and SOH step =0.8, calculating a reference value SOC of state of charge step (1) = 0.368706354120217, and so on, can calculate SOC step (2)=0.381193148080227。
Step S1022: and acquiring corresponding battery open-circuit voltage, ohmic impedance and polarization impedance from the calibrated battery parameter table according to the reference value.
Obtaining a reference value according to the steps, and obtaining the SOC by calibrating a battery parameter table and a linear difference value step (1) In the case of= 0.368706354120217, the battery open circuit voltage ocv= 3.8354, the ohmic impedance r0= 0.0407, the polarization impedance r1=0.025, and the constant current i= 2.453.
And so on, at SOC step (2) In the case of = 0.381193148080227, the corresponding cell open circuit voltage ocv=3.84, ohmic resistance r0=0.0408, polarization resistance r1=0.025, constant current i= 2.453 are obtained by calibrating the cell parameters as well.
Step S1023: and calculating simulation voltages corresponding to the reference values according to the open-circuit voltage of the battery, the ohmic impedance and the polarization impedance.
Further, by formula V simulate =ocv+i (r0+r1), substituting the corresponding cell open-circuit voltage, ohmic impedance, polarization impedance and constant current to obtain V simulate (1)= 3.9966，V simulate (2) In the example of the present invention, 36V groups were obtained by calculation in this order = 4.0015 simulate 。
Step S1024: and calculating the simulation voltage difference of the adjacent reference values to obtain the simulation voltage difference value.
The simulated voltage difference is further calculated by the following formula:
substituting the result of the above steps to obtainTo the simulated voltage difference：
And the like to obtain other simulation voltage differences, and the specific calculation process is not repeated here.
Step S103: and calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result.
By the description of the above embodiment, the error of the sampling voltage difference and the simulation voltage difference is calculated as a result of the same steps as described above. Referring to fig. 4, a flow chart of an error calculation method according to an embodiment of the invention is shown, and the method includes the following steps.
Step S1031: differences between the respective sampled voltage differences and the simulated voltage differences are calculated.
In an exemplary embodiment, a simulated voltage difference is obtained based on the calculation results of the above stepsSample voltage difference =0.0049= 0.00300000000000011, this gives the difference:
and the like, other differences between the simulation voltage difference value and the sampling voltage difference value are obtained.
Step S1032: and summing the absolute values of the differences to obtain the error.
Calculating the sum of the absolute values of the difference between the simulation voltage and the difference between the sampling voltage, and describing the error between the difference between the sampling voltage and the difference between the simulation voltage by using the sum as a cost function：
Similarly further calculate otherError in case ofThe results obtained are shown in the following table:
further from the above calculation resultWhen =89%, i.e., 89% of the battery state of health, the corresponding error is minimal, resulting in an estimated battery state of health of 89%.
In addition, in order to further improve the calculation efficiency, the embodiment of the invention can adopt dynamic step length, in particular, the battery health stateIncluding values between 80% and 100% that change in dynamic steps that are positively correlated with the rate of error change corresponding to the last battery state of health. Specifically, when the error obtained by the last calculation of the battery health state becomes smaller, the dynamic step size is reduced; when the error obtained by the last calculation of the battery state of health becomes larger, the dynamic step length is increased, so that the battery state of health corresponding to the minimum error is converged fastest, unnecessary calculation consumption is reduced, and the calculation efficiency is improved.
Referring to fig. 5 to 7, fig. 5 shows a simulation curve in which the battery state of health is increased from 80% to 100% in a gradient of 1%, and a sampling curve, and it can be seen from fig. 5 that the simulation curve and the sampling curve have the smallest error when the battery state of health is 89%. In connection with fig. 7, a simulation curve (e.g., deltaV-Model) and a sampling curve (e.g., deltaV-Sample arrow) of 89% of the battery state of health are shown, and it is known that the simulation best matches the actual sampling result when the battery state of health is 89%. Fig. 6 further verifies that the Model established in the embodiment of the present invention matches the voltage characteristic curve (V-Model) obtained by sampling (V-Sample) in the case where the battery state of health is 89%, so that it is advantageous to prove that the estimated battery state of health in the embodiment of the present invention matches the test result, and that the estimation accuracy is improved.
As can be seen from the description of the above embodiments, the method for estimating the state of health of a battery according to the embodiments of the present invention includes obtaining sampling voltage differences corresponding to a plurality of states of charge; under the condition of a plurality of battery health states, calculating corresponding simulation voltage difference values according to the plurality of charge states; and calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result. According to the invention, the voltage difference value is subjected to model simulation, the battery health state is estimated through error numerical calculation, and the calculated value with the smallest error is used as an estimation result, so that the rapid model simulation is realized, the resource occupation is small, and the estimation efficiency and accuracy are effectively improved.
From the above description of the method embodiments, it will be clear to those skilled in the art that the present invention may be implemented by means of software plus necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present invention may be embodied essentially or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like.
Embodiments of the present invention provide a non-volatile computer storage medium storing computer-executable instructions that are operable to perform the battery state of health estimation method of any of the method embodiments described above.
Corresponding to the embodiment of the battery state of health estimation method provided by the invention, the invention also provides a battery state of health estimation device.
Referring to fig. 8, a schematic structural diagram of a battery state of health estimation device according to an embodiment of the present invention is shown, where the device includes:
the sampling module 11 is configured to obtain sampling voltage differences corresponding to a plurality of states of charge; comprising the following steps: acquiring an initial value meeting a battery standing condition; when the battery meets the temperature condition and is charged with constant current, calculating the state of charge according to the initial value; when the battery constant current charge passes through the preset polarization time and the state of charge is larger than a preset state of charge threshold value, acquiring a sampling voltage corresponding to each state of charge, and taking the difference of adjacent sampling voltages as a sampling voltage difference value of the corresponding state of charge;
the simulation module 12 is configured to calculate a corresponding simulation voltage difference value according to the plurality of states of charge under the condition of the plurality of battery states of health; comprising the following steps: calculating reference values of all states of charge according to the state of health of the battery; acquiring corresponding battery open-circuit voltage, ohmic impedance and polarization impedance from a calibrated battery parameter table according to the reference value; according to the open-circuit voltage of the battery, the ohmic impedance and the polarization impedance, calculating simulation voltages corresponding to all reference values; calculating simulation voltage differences of adjacent reference values to obtain simulation voltage difference values;
and a result module 13, configured to calculate errors of the respective sampled voltage differences and the simulated voltage differences, and take the battery state of health with the smallest error as an estimation result.
Fig. 9 is a schematic hardware structure of an electronic device for performing a battery state of health estimation method according to an embodiment of the present invention, as shown in fig. 9, where the device includes:
one or more processors 910, and a memory 920, one processor 910 being illustrated in fig. 9.
The apparatus for performing the battery state of health estimation method may further include: an input device 930, and an output device 940.
The processor 910, memory 920, input device 930, and output device 940 may be connected by a bus or other means, for example in fig. 9.
The memory 920 is used as a non-volatile computer readable storage medium, and may be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as program instructions/modules (e.g., the sampling module 11, the simulation module 12, and the result module 13 shown in fig. 8) corresponding to the battery state of health estimation method in the embodiment of the present invention. The processor 910 executes various functional applications of the server and data processing by running non-volatile software programs, instructions and modules stored in the memory 920, i.e., implements the battery state of health estimation method of the method embodiments described above.
Memory 920 may include a storage program area that may store an operating system, at least one application required for functionality, and a storage data area; the storage data area may store data created from the use of the processing device estimated from the state of health of the battery, etc. In addition, memory 920 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 920 may optionally include memory remotely located with respect to processor 910, which may be connected to the battery state of health estimation processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 930 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the processing device for battery state of health estimation. The output device 940 may include a display device such as a display screen.
The one or more modules are stored in the memory 920 that, when executed by the one or more processors 910, perform the battery state of health estimation method of any of the method embodiments described above.
The product can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
The electronic device of the embodiments of the present invention exists in a variety of forms including, but not limited to:
(1) A mobile communication device: such devices are characterized by mobile communication capabilities and are primarily aimed at providing voice, data communications. Such terminals include: smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones, etc.
(2) Ultra mobile personal computer device: such devices are in the category of personal computers, having computing and processing functions, and generally also having mobile internet access characteristics. Such terminals include: PDA, MID, and UMPC devices, etc., such as iPad.
(3) Portable entertainment device: such devices may display and play multimedia content. The device comprises: audio, video players (e.g., iPod), palm game consoles, electronic books, and smart toys and portable car navigation devices.
(4) And (3) a server: the configuration of the server includes a processor, a hard disk, a memory, a system bus, and the like, and the server is similar to a general computer architecture, but is required to provide highly reliable services, and thus has high requirements in terms of processing capacity, stability, reliability, security, scalability, manageability, and the like.
(5) Other electronic devices with data interaction function.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A battery state of health estimation method, comprising:
obtaining sampling voltage differences corresponding to a plurality of states of charge includes: acquiring an initial value meeting a battery standing condition; when the battery meets the temperature condition and is charged with constant current, calculating the state of charge according to the initial value; when the battery constant current charge passes through the preset polarization time and the state of charge is larger than a preset state of charge threshold value, acquiring a sampling voltage corresponding to each state of charge, and taking the difference of adjacent sampling voltages as a sampling voltage difference value of the corresponding state of charge;
under the condition of a plurality of battery health states, calculating corresponding simulation voltage difference values according to the plurality of charge states, wherein the simulation voltage difference values comprise: calculating reference values of all states of charge according to the state of health of the battery; acquiring corresponding battery open-circuit voltage, ohmic impedance and polarization impedance from a calibrated battery parameter table according to the reference value; according to the open-circuit voltage of the battery, the ohmic impedance and the polarization impedance, calculating simulation voltages corresponding to all reference values; calculating simulation voltage differences of adjacent reference values to obtain simulation voltage difference values;
and calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result.
2. The battery state of health estimation method of claim 1, wherein the state of charge is calculated as follows:
SOC(K)=SOC(K-1)+I/(CN*3600)，
wherein CN represents the rated capacity of the battery, I represents the constant current charging current, K is a natural number equal to or greater than 1, and SOC (0) represents the initial value.
3. The method of claim 2, wherein the predetermined state of charge threshold is between 30% and 60%, and the predetermined polarization time is 2 to 5 times the battery equivalent circuit time constant.
4. A battery state of health estimation method according to any one of claims 1 to 3, wherein the reference value calculation formula of the state of charge is as follows:，
wherein i=1, 2 … m, m is the number of simulation points; SOC (State of Charge) step (i) Is a reference value of state of charge, SOC (i) represents one of the plurality of states of charge, SOC Initial Is the initial value, SOH step Representing one of the plurality of battery states of health.
5. The method of claim 1, wherein the plurality of battery states of health comprises a value between 80% and 100% and increasing in 1% steps.
6. The method of claim 1, wherein the plurality of battery states of health comprises a value ranging from 80% to 100% and varying in a dynamic step size that is positively correlated with a rate of change of error corresponding to a last battery state of health.
7. A battery state of health estimation method according to any one of claims 1 to 3, wherein said calculating the error of each of the sampling voltage difference and the simulation voltage difference comprises:
calculating the difference between each sampling voltage difference value and the simulation voltage difference value;
and summing the absolute values of the differences to obtain the error.
8. A battery state of health estimation apparatus, comprising:
the sampling module is used for obtaining sampling voltage difference values corresponding to a plurality of charge states, and comprises the following components: acquiring an initial value meeting a battery standing condition; when the battery meets the temperature condition and is charged with constant current, calculating the state of charge according to the initial value; when the battery constant current charge passes through the preset polarization time and the state of charge is larger than a preset state of charge threshold value, acquiring a sampling voltage corresponding to each state of charge, and taking the difference of adjacent sampling voltages as a sampling voltage difference value of the corresponding state of charge;
the simulation module is used for calculating corresponding simulation voltage difference values according to the multiple states of charge under the condition of the multiple states of battery health, and comprises the following steps: calculating reference values of all states of charge according to the state of health of the battery; acquiring corresponding battery open-circuit voltage, ohmic impedance and polarization impedance from a calibrated battery parameter table according to the reference value; according to the open-circuit voltage of the battery, the ohmic impedance and the polarization impedance, calculating simulation voltages corresponding to all reference values; calculating simulation voltage differences of adjacent reference values to obtain simulation voltage difference values;
and the result module is used for calculating errors of the sampling voltage difference values and the simulation voltage difference values, and taking the battery health state with the minimum error as an estimation result.
9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the battery state of health estimation method of any of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, characterized in that at least one section of program is stored in the storage medium, which is loaded and executed by a processor to implement the steps of the battery state of health estimation method according to any one of claims 1 to 7.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104535932A (en) * | 2014-12-20 | 2015-04-22 | 吉林大学 | Lithium ion battery charge state estimating method |
| CN104833921A (en) * | 2014-12-01 | 2015-08-12 | 北汽福田汽车股份有限公司 | Battery pack charge state calculating method and calculating device |
| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN109033532A (en) * | 2018-06-29 | 2018-12-18 | 北京航空航天大学 | A kind of zero voltage switch phase-shifting full-bridge power supply health state evaluation method |
| CN109256834A (en) * | 2018-10-12 | 2019-01-22 | 华南理工大学 | Battery pack active equalization method based on cell health state and state-of-charge |
| CN110967636A (en) * | 2019-06-24 | 2020-04-07 | 宁德时代新能源科技股份有限公司 | Battery state of charge correction method, device and system and storage medium |
| CN111581904A (en) * | 2020-04-17 | 2020-08-25 | 西安理工大学 | Lithium battery SOC and SOH collaborative estimation method considering influence of cycle number |
| CN113176505A (en) * | 2021-04-30 | 2021-07-27 | 重庆长安新能源汽车科技有限公司 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
| WO2022136098A1 (en) * | 2020-12-21 | 2022-06-30 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Method for estimating the lifespan of an energy storage system |
| CN115436806A (en) * | 2022-08-27 | 2022-12-06 | 湖州师范学院 | SOC and SOH self-adaptive collaborative estimation method of lithium ion battery |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3050278B1 (en) * | 2016-04-15 | 2018-03-30 | Saft | METHOD FOR DETERMINING THE VALUE OF PARAMETERS RELATING TO THE STATUS OF AN BATTERY BATTERY BATTERY BATTERY AND ELECTRONIC BATTERY MANAGEMENT SYSTEM |
| DE102017211506A1 (en) * | 2017-07-06 | 2019-01-10 | Lithium Energy and Power GmbH & Co. KG | Method for determining the state of an electrical energy storage unit, corresponding device for carrying out the method and corresponding electrical energy storage unit |
| JP7232548B2 (en) * | 2019-11-29 | 2023-03-03 | ミンテク カンパニー リミテッド | Battery state estimation device and method |
-
2023
- 2023-08-30 CN CN202311098894.8A patent/CN116840699B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015188610A1 (en) * | 2014-06-11 | 2015-12-17 | 北京交通大学 | Method and device for estimating state of charge of battery |
| CN104833921A (en) * | 2014-12-01 | 2015-08-12 | 北汽福田汽车股份有限公司 | Battery pack charge state calculating method and calculating device |
| CN104535932A (en) * | 2014-12-20 | 2015-04-22 | 吉林大学 | Lithium ion battery charge state estimating method |
| CN109033532A (en) * | 2018-06-29 | 2018-12-18 | 北京航空航天大学 | A kind of zero voltage switch phase-shifting full-bridge power supply health state evaluation method |
| CN109256834A (en) * | 2018-10-12 | 2019-01-22 | 华南理工大学 | Battery pack active equalization method based on cell health state and state-of-charge |
| CN110967636A (en) * | 2019-06-24 | 2020-04-07 | 宁德时代新能源科技股份有限公司 | Battery state of charge correction method, device and system and storage medium |
| CN111581904A (en) * | 2020-04-17 | 2020-08-25 | 西安理工大学 | Lithium battery SOC and SOH collaborative estimation method considering influence of cycle number |
| WO2022136098A1 (en) * | 2020-12-21 | 2022-06-30 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Method for estimating the lifespan of an energy storage system |
| CN113176505A (en) * | 2021-04-30 | 2021-07-27 | 重庆长安新能源汽车科技有限公司 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
| CN115436806A (en) * | 2022-08-27 | 2022-12-06 | 湖州师范学院 | SOC and SOH self-adaptive collaborative estimation method of lithium ion battery |
Non-Patent Citations (2)
| Title |
|---|
| 基于温度变化率曲线的锂离子电池健康状态评估算法;党月懋;电气工程学报;第17卷(第3期);58-65 * |
| 锂离子电池建模现状研究综述;李建林;热力发电;第50卷(第7期);1-7 * |
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