CN114460484A - Rechargeable battery service life prediction method and device based on accumulated loss - Google Patents
Rechargeable battery service life prediction method and device based on accumulated loss Download PDFInfo
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Abstract
The invention belongs to the technical field related to service life prediction of rechargeable batteries, and discloses a rechargeable battery service life prediction method and device based on accumulated loss, which are used for coping with random charging and discharging scenes widely existing in the actual use process of a rechargeable battery. The method comprises the following steps: (1) acquiring or estimating the accumulated consumption of the rechargeable battery as the current life; (2) obtaining or estimating a key performance index of the rechargeable battery as a current SOH; (3) acquiring a degradation model of the rechargeable battery; (4) predicting an estimated value of the residual life of the rechargeable battery according to the current SOH, the current life, the degradation model and a preset failure index; the conventional method for predicting the service life of the rechargeable battery adopts the number of charge and discharge cycles as the service life, and only considers the charge and discharge process under ideal conditions. In the random charge and discharge process of practical application, the prediction effect by using the service life index is poor. The invention adopts the accumulated loss of the rechargeable battery as the service life index, and aims to solve the problem of service life prediction of the rechargeable battery under the actual use condition. The invention can directly predict the residual quantity of the accumulated loss, thereby being closer to the reality. Under the condition of random use, compared with a method for predicting the service life by taking the number of charge-discharge cycles as a prediction method, the accuracy of the method can be improved by more than 80 percent.
Description
Technical Field
The invention belongs to the technical field related to service life prediction of rechargeable batteries, and particularly relates to a rechargeable battery service life prediction method and device based on accumulated loss.
Background
With the development of technology, the fields to which the rechargeable battery can be applied are increasing. While the performance of the rechargeable battery gradually decays during use, which affects its operating performance to some extent. When the battery is attenuated to a certain extent, it cannot be effectively used. The service life condition of the battery is effectively monitored and predicted, so that the stability and the safety of the battery in the working process can be ensured, and meanwhile, the maintenance and replacement work can be reasonably arranged.
In conventional methods for predicting the life of a rechargeable battery, the number of charge/discharge cycles is used as the life. These conventional methods of predicting rechargeable battery life are derived from testing rechargeable battery life under laboratory conditions. Under the laboratory condition, the charging process and the discharging process can be ensured to be alternately carried out, and the integrity of the charging process and the discharging process can be ensured. Therefore, the number of charge and discharge cycles is adopted as the life with good accuracy.
However, in the actual use process of the rechargeable battery, the charging and discharging process depends on the use habit of the user. Therefore, in most cases, the charging process and the discharging process are discontinuous and incomplete. For example, when a contact failure occurs in a charging wire of a user, several tens of charge and discharge cycles may occur within one day. When the number of charge and discharge cycles is used as the lifetime, the predicted lifetime is rapidly decreased, but the actual lifetime is not greatly changed. Meanwhile, the half-filling and half-discharging phenomenon which exists in a large number in the use scene of the user also influences the prediction accuracy.
Obviously, the number of charge and discharge cycles is not accurate and reasonable in the practical use of the rechargeable battery.
Disclosure of Invention
After extensive analysis and research, the inventor finds that the accumulated loss is very suitable for describing the degradation process of the rechargeable battery under random charge and discharge conditions by taking the accumulated loss as the service life.
In view of the above, the invention discloses a rechargeable battery service life prediction method based on accumulated loss, which can accurately predict the service life of a rechargeable battery in the actual use process, thereby giving an early warning in time and ensuring the safety in the use process of the rechargeable battery. Compared with the method based on the cycle times, the method has the advantage that the accuracy is improved by over 80 percent.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for predicting a lifetime of a rechargeable battery based on an accumulated amount of wear, including the steps of:
acquiring or estimating the accumulated consumption of the rechargeable battery as the current life;
obtaining or estimating a key performance index of the rechargeable battery as a current SOH;
acquiring a degradation model of the rechargeable battery;
and predicting the estimated value of the residual life of the rechargeable battery according to the current SOH, the current life, the degradation model and a preset failure index.
In some embodiments, the accumulated amount of wear includes at least one of an accumulated charge amount, an accumulated discharge amount, an accumulated charge and discharge amount, and the like;
the accumulated loss amount comprises at least one constant multiple mathematical transformation of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the key performance index comprises at least one of maximum electricity storage capacity, attenuation of the maximum electricity storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the key performance indexes comprise constant multiple mathematical transformation of at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like.
In some embodiments, the accumulated amount of wear includes at least one of an accumulated amount of work for the electricity consuming equipment to normally operate by the rechargeable battery, an accumulated amount of mileage for the automobile to normally travel by the rechargeable battery, and the like;
the accumulated loss amount comprises at least one constant multiple mathematical transformation of accumulated working amount of the rechargeable battery for normal operation of power consumption equipment, accumulated mileage amount of the rechargeable battery for normal running of the automobile and the like;
the key performance indexes comprise at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like;
the key performance indexes comprise constant multiple mathematical transformation of at least one of the maximum power storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum power storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like.
In some embodiments, the step of obtaining a degradation model of the rechargeable battery comprises:
acquiring a preset degradation model;
acquiring historical use data of the rechargeable battery and constructing a degradation model;
and acquiring historical use data of other rechargeable batteries of the same type and constructing a degradation model.
In some embodiments, the failure indicator is preset in advance as a certain value in the SOH value range of the rechargeable battery, and the rechargeable battery fails when the SOH reaches the indicator;
the residual life is a residual accumulative amount of the accumulative consumption, namely the accumulative consumption which can be additionally accumulated in a range from the forecast starting time to the time when the rechargeable battery is failed;
the current life is a current value of the accumulated amount of wear, that is, an accumulated amount of wear accumulated in a range from the time when the rechargeable battery is put into use to the prediction start time.
In some embodiments, obtaining an estimated value of the total service life of the rechargeable battery according to the current SOH, the current service life, a degradation model and a preset failure index;
obtaining a corresponding SOH within a remaining life range according to a current SOH, a current life, a degradation model and a preset failure index;
outputting planned replacement time according to at least one of seven indexes, such as current SOH, current service life, a degradation model, preset failure indexes, residual service life, total service life and corresponding SOH in a residual service life range;
the total life includes an accumulated amount of wear within a range from when the rechargeable battery is put into use to when the SOH reaches a failure index;
the residual life of the rechargeable battery also comprises the ratio of the residual accumulative amount of the accumulative consumption to the total life, namely the ratio of the accumulative consumption which can be additionally accumulated to the total life in the range from the prediction starting time to the time when the rechargeable battery is invalid;
the SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order.
According to a second aspect of the embodiments of the present disclosure, there is provided a rechargeable battery life prediction apparatus based on an accumulated amount of wear, including:
an accumulated amount of wear acquisition module configured to acquire or estimate an accumulated amount of wear of the rechargeable battery as a current life;
a key performance index acquisition module configured to acquire or estimate a key performance index of the rechargeable battery as a current SOH;
a model acquisition module configured to acquire a degradation model of the rechargeable battery;
and the residual life prediction module is configured to predict the estimated value of the residual life of the rechargeable battery according to the current SOH, the current life, the degradation model and a preset failure index.
In some embodiments, the accumulated amount of wear obtained by the accumulated amount of wear obtaining module includes at least one of an accumulated charge amount, an accumulated discharge amount, an accumulated charge and discharge amount, and the like;
the accumulated loss amount obtained by the accumulated loss amount obtaining module comprises constant times mathematical transformation of at least one of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the key performance index acquired by the key performance index acquisition module comprises at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the key performance index acquired by the key performance index acquisition module comprises constant multiple mathematical transformation of at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the accumulated consumption acquired by the accumulated consumption acquisition module comprises at least one of accumulated working capacity accumulated by the normal operation of the rechargeable battery for the power consumption equipment, accumulated mileage accumulated by the normal running of the rechargeable battery for the automobile and the like;
the accumulated consumption obtained by the accumulated consumption obtaining module comprises constant times mathematical transformation of at least one of accumulated working capacity of the rechargeable battery for normal operation of the power consumption equipment, accumulated mileage of the rechargeable battery for normal running of the automobile and the like;
the key performance index acquired by the key performance index acquisition module comprises at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like;
the key performance index acquired by the key performance index acquisition module comprises constant times mathematical transformation of at least one of the maximum power storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum power storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like.
In some embodiments, the model obtaining module obtains a function of a degradation model of the rechargeable battery, including obtaining a preset degradation model; acquiring historical use data of the rechargeable battery and constructing a degradation model; acquiring historical use data of other rechargeable batteries of the same type and constructing a degradation model;
the failure index is preset as a certain value in the SOH value range of the rechargeable battery in advance, and when the SOH reaches the index, the rechargeable battery fails;
the residual life is a residual accumulative amount of the accumulative consumption, namely the accumulative consumption which can be additionally accumulated in a range from the forecast starting time to the time when the rechargeable battery is failed;
the current life is a current value of the accumulated amount of wear, that is, an accumulated amount of wear accumulated in a range from the time when the rechargeable battery is put into use to the prediction start time.
In some embodiments, the system further comprises a total life prediction module configured to obtain an estimated value of the total life of the rechargeable battery according to the current SOH, the current life, a degradation model and a preset failure index;
the SOH prediction module is configured to obtain the corresponding SOH within the remaining life range according to the current SOH, the current life, the degradation model and a preset failure index;
the system also comprises a planning module which is configured to output the planned replacement time according to at least one of seven indexes, such as the current SOH, the current service life, a degradation model, a preset failure index, the remaining service life, the total service life and the corresponding SOH in the remaining service life range;
the total life includes an accumulated amount of wear within a range from when the rechargeable battery is put into use to when the SOH reaches a failure index;
the residual life of the rechargeable battery also comprises the ratio of the residual accumulative amount of the accumulative consumption to the total life, namely the ratio of the accumulative consumption which can be additionally accumulated to the total life in the range from the prediction starting time to the time when the rechargeable battery is invalid;
the SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order.
Drawings
The above and additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method of predicting a life of a rechargeable battery based on an accumulated amount of wear, according to some embodiments of the present disclosure;
fig. 2 is a structural frame diagram of a device for predicting the life of a rechargeable battery based on the accumulated amount of wear according to some embodiments of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the disclosure, its application, or uses. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials and values set forth in these embodiments are to be construed as illustrative only and not as limiting unless otherwise specifically stated.
The use of the word "comprising" or "comprises" and the like in this disclosure means that the elements listed before the word encompass the elements listed after the word and do not exclude the possibility that other elements may also be encompassed.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In conventional methods for predicting the life of a rechargeable battery, the number of charge/discharge cycles is used as the life. These conventional methods of predicting rechargeable battery life are derived from testing rechargeable battery life under laboratory conditions. Under the laboratory condition, the charging process and the discharging process can be ensured to be alternately carried out, and the integrity of the charging process and the discharging process can be ensured. Therefore, the number of charge and discharge cycles is adopted as the life with good accuracy. However, this method is difficult to be applied in practice because it does not take into account randomness and incompleteness of the user in the actual use process.
The present disclosure provides a method and an apparatus for predicting the life of a rechargeable battery based on an accumulated amount of wear, which can solve the randomness and the incompleteness of the rechargeable battery in the actual use process.
Fig. 1 is a flow chart of a method for predicting a lifetime of a rechargeable battery based on an accumulated amount of wear, according to some embodiments of the present disclosure. In some embodiments, the lifetime prediction method comprises steps 101-107.
In step 101, the accumulated amount of wear of the rechargeable battery is obtained or estimated as the current life.
The "obtaining or estimating" in this step includes that, if the accumulated amount of wear cannot be obtained due to a special condition, the accumulated amount of wear may be estimated according to historical data, which is not limited in this application.
At step 103, a key performance indicator of the rechargeable battery is obtained or estimated as the current SOH.
The "obtaining or estimating" in this step includes that, if the key performance index cannot be obtained due to a specific situation, the key performance index can be estimated according to historical data, which is not limited in this application.
In some embodiments, the accumulated amount of wear includes at least one of an accumulated amount of charge, an accumulated amount of discharge, an accumulated amount of charge and discharge, and the like;
the accumulated loss amount comprises at least one constant multiple mathematical transformation of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the key performance index comprises at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the key performance indexes comprise constant multiple mathematical transformation of at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like.
The actual meaning of the accumulated amount of wear will be described with the accumulated amount of charge as an example. The accumulated charge amount indicates an amount of electricity accumulated in the rechargeable battery from the time of use to the time of the prediction start. For the rechargeable battery, the rechargeable battery is continuously charged or discharged from the beginning of being put into use, and the required accumulated charging amount is obtained by accumulating the electric quantity charged in each charging process.
The maximum electricity storage capacity is the maximum electric quantity which can be charged in the charging process of the rechargeable battery, and the maximum electricity storage capacity can be attenuated along with the use process of the rechargeable battery. A rechargeable battery generally has a rated index such as a rated capacity or a rated operation amount. In many application scenarios, the key performance indexes such as the charge amount and the workload are normalized according to the rating index and the like to obtain the indexes such as the charge amount and the workload in a relative sense. For example, the maximum power storage capacity herein includes an absolute maximum power storage capacity, and also includes a relative maximum power storage capacity obtained by dividing the maximum power storage capacity by a rated capacity (i.e., a constant-multiple mathematical transformation).
Similarly, the accumulated amount of wear may include a constant-factor mathematical transformation of an index such as an accumulated charge amount, an accumulated discharge amount, and an accumulated charge-discharge amount. For example, in some cases, one integrated charge coefficient may be obtained by dividing the integrated charge amount by the rated capacity of the rechargeable battery, and the integrated charge coefficient may be used as the integrated wear amount. The definition of the constant multiple mathematical transformation relating to both the accumulated discharge amount and the accumulated charge-discharge amount is similar to that.
The decrement of the maximum stored energy capacity represents the decrement of the maximum stored energy capacity obtained as compared with the case where the rechargeable battery is just put into use. The method for obtaining the maximum power storage capacity attenuation condition includes an absolute attenuation obtained by subtracting the current maximum power storage capacity from the maximum power storage capacity in the initial state, and also includes an absolute attenuation obtained by subtracting the current maximum power storage capacity from the rated capacity, which is not limited in this application. Here, the attenuation of the maximum stored energy amount includes an absolute attenuation of the maximum stored energy amount, and also includes a relative attenuation rate (i.e., constant-multiple mathematical transformation) obtained by dividing the absolute attenuation by the rated capacity.
In addition, the internal resistance of the secondary battery also changes during use, and the amount of change in the internal resistance of the secondary battery indicates a change in the internal resistance of the secondary battery compared to when the secondary battery is just put into use. The change of the internal resistance of the battery includes the absolute change of the resistance and also includes the change rate obtained by dividing the absolute change by the initial resistance (namely, constant times mathematical transformation).
In some embodiments, the accumulated amount of wear includes at least one of an accumulated amount of work for the electricity consuming equipment to normally operate by the rechargeable battery, an accumulated amount of mileage for the automobile to normally travel by the rechargeable battery, and the like;
the accumulated loss amount comprises at least one constant multiple mathematical transformation of accumulated working amount of the rechargeable battery for normal operation of power consumption equipment, accumulated mileage amount of the rechargeable battery for normal running of the automobile and the like;
the key performance indexes comprise at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like;
the key performance indexes comprise constant multiple mathematical transformation of at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like.
For some power consuming devices, the amount of work accumulated or generated during the use of the rechargeable battery can be very convenient to measure and obtain. For example, the maximum storage capacity of the rechargeable battery can be used for the workload generated by the operation of the power consumption equipment, and the maximum storage capacity of the rechargeable battery can be used for the mileage generated by the driving of the automobile. The rechargeable battery supplies the accumulated work load of the normal operation of the power consumption equipment and the accumulated mileage of the normal running of the automobile. The index is directly related to the performance of the rechargeable battery, and therefore, the index can be used as a key performance index or can be accumulated to obtain an accumulated loss amount. The index is also applicable to the related definitions such as the rated index and constant multiple mathematical transformation.
At step 105, a degradation model of the rechargeable battery is obtained.
In some embodiments, the step of obtaining a degradation model of the rechargeable battery comprises:
acquiring a preset degradation model;
acquiring historical use data of the rechargeable battery and constructing a degradation model;
and acquiring historical use data of other rechargeable batteries of the same type and constructing a degradation model.
For a rechargeable battery, the degradation model may be set in advance, and thus may be directly obtained. Meanwhile, the degradation rule can be estimated from the historical use data of the rechargeable battery, so that a degradation model can be constructed in real time according to the historical data before the prediction process is started. Besides, the degradation model can be constructed by acquiring historical use data of other rechargeable batteries of the same type. For example, the data is collected by performing charge and discharge tests on the same type of rechargeable batteries, or the use data of the same type of rechargeable batteries used by other users is collected. The same type of rechargeable batteries herein includes rechargeable batteries of the same type, and also rechargeable batteries of the same manufacturing process and material.
In step 107, an estimated value of the remaining life of the rechargeable battery is predicted according to the current SOH, the current life, the degradation model and a preset failure index.
In some embodiments, the failure indicator is preset in advance as a certain value in the SOH value range of the rechargeable battery, and the rechargeable battery fails when the SOH reaches the indicator;
the residual life is a residual accumulative amount of the accumulative consumption, namely the accumulative consumption which can be additionally accumulated in a range from the forecast starting time to the time when the rechargeable battery is failed;
the current life is a current value of the accumulated amount of wear, that is, an accumulated amount of wear accumulated in a range from the time when the rechargeable battery is put into use to the prediction start time.
The failure indicator may be a predetermined limit, for example, a value in the range of the SOH value of the rechargeable battery. The remaining life is a remaining accumulative amount of the accumulative consumption, for example, an accumulative consumption which can be additionally accumulated in a range from the prediction start time to the time when the rechargeable battery is failed. For example, the accumulated charge amount is set as the current life, and the preset failure index is 20% of the initial capacity. For a rechargeable battery with a rated capacity of 1000Mah, the failure limit is 200 Mah. After a long period of use, the cumulative amount of charge accumulated at present is 10000Mah, and the maximum current storage capacity is 600Mah, that is, the decrement in the maximum current storage capacity is 400 Mah. In this case, the maximum cumulative capacity of the rechargeable battery is again attenuated by 400Mah, and the failure limit of 200Mah is reached. Based on a simple linear mathematical model and according to the historical usage data of the battery, if the battery is to be attenuated by 400Mah, an additional cumulative charge of 10000Mah is still required, and thus the remaining usable life of the battery is 10000 Mah.
In some embodiments, obtaining an estimated value of the total service life of the rechargeable battery according to the current SOH, the current service life, a degradation model and a preset failure index;
obtaining a corresponding SOH within a remaining life range according to a current SOH, a current life, a degradation model and a preset failure index;
outputting planned replacement time according to at least one of seven indexes, such as current SOH, current service life, a degradation model, preset failure indexes, residual service life, total service life and corresponding SOH in a residual service life range;
the total life includes an accumulated amount of wear within a range from when the rechargeable battery is put into use to when the SOH reaches a failure index;
the residual life of the rechargeable battery also comprises the ratio of the residual accumulative amount of the accumulative consumption to the total life, namely the ratio of the accumulative consumption which can be additionally accumulated to the total life in the range from the prediction starting time to the time when the rechargeable battery is invalid;
the SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the prediction start time to the time when the rechargeable battery is out of order.
For the rechargeable battery, degradation will occur continuously since the rechargeable battery is put into use, and when the SOH value reaches a preset failure index, the corresponding accumulated loss amount can be regarded as the total life, i.e. the accumulated loss amount in the range from the time the rechargeable battery is put into use to the time the SOH reaches the failure index. In addition, the remaining life may include the remaining life in a relative sense, i.e., the ratio of the remaining accumulative amount of the accumulative wear amount to the total life. For example, 30% of the cumulative amount of wear that can be accumulated additionally remains.
The accumulated amount of wear will increase continuously as the rechargeable battery is used continuously, so the present invention uses the accumulated amount of wear as the life index. In the future, as long as the rechargeable battery has not failed, the rechargeable battery can be used continuously, and therefore the accumulated amount of wear continues to accumulate. The SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order. The description of "at least one" is used herein and thus includes any one or more of the corresponding SOHs over the remaining life span. The planned replacement time is output to remind a user in time before the battery fails. For example, when the acquired remaining life is insufficient, the user needs to be reminded to replace the rechargeable battery. Alternatively, the user may be informed of the ideal battery replacement time by calculating it in advance.
Fig. 2 is a flowchart of a device for predicting a lifetime of a rechargeable battery based on an accumulated amount of wear according to some embodiments of the present disclosure. In some embodiments, the life prediction device includes an accumulated amount of wear acquisition module, a key performance index acquisition module, a model acquisition module, and a remaining life prediction module.
The accumulated amount of wear acquisition module 201 is configured to acquire or estimate the accumulated amount of wear of the rechargeable battery as the current life, for example, step 101 is executed.
The module has "obtaining or estimating" includes that if the accumulated amount of wear cannot be obtained due to a special condition, the module can estimate based on the historical data, which is not limited in this application.
The key performance indicator obtaining module 203 is configured to obtain or estimate the key performance indicator of the rechargeable battery as the current SOH, for example, execute step 103.
The "obtaining or estimating" function of the module includes that, if the key performance index cannot be obtained due to a specific condition, the module can estimate according to historical data, which is not limited in this application.
In some embodiments, the accumulated amount of wear obtained by the accumulated amount of wear obtaining module includes at least one of an accumulated charge amount, an accumulated discharge amount, an accumulated charge and discharge amount, and the like;
the accumulated loss amount obtained by the accumulated loss amount obtaining module comprises constant times mathematical transformation of at least one of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the key performance index acquired by the key performance index acquisition module comprises at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the key performance index acquired by the key performance index acquisition module comprises constant multiple mathematical transformation of at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the accumulated consumption acquired by the accumulated consumption acquisition module comprises at least one of accumulated working capacity accumulated by the normal operation of the rechargeable battery for the power consumption equipment, accumulated mileage accumulated by the normal running of the rechargeable battery for the automobile and the like;
the accumulated consumption obtained by the accumulated consumption obtaining module comprises constant times mathematical transformation of at least one of accumulated working capacity of the rechargeable battery for normal operation of the power consumption equipment, accumulated mileage of the rechargeable battery for normal running of the automobile and the like;
the key performance index acquired by the key performance index acquisition module comprises at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like;
the key performance index acquired by the key performance index acquisition module comprises constant times mathematical transformation of at least one of the maximum power storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum power storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like.
The actual meaning of the accumulated amount of wear will be described with the accumulated amount of charge as an example. The accumulated charge amount indicates an amount of electricity accumulated in the rechargeable battery from the time of use to the time of the prediction start. For the rechargeable battery, the rechargeable battery is continuously charged or discharged from the beginning of being put into use, and the required accumulated charging amount is obtained by accumulating the electric quantity charged in each charging process.
The maximum electricity storage capacity is the maximum electric quantity which can be charged in the charging process of the rechargeable battery, and the maximum electricity storage capacity can be attenuated along with the use process of the rechargeable battery. A rechargeable battery generally has a rated index such as a rated capacity or a rated operation amount. In many application scenarios, the key performance indexes such as the charge amount and the workload are normalized according to the rating index and the like to obtain the indexes such as the charge amount and the workload in a relative sense. For example, the maximum power storage capacity herein includes an absolute maximum power storage capacity, and also includes a relative maximum power storage capacity obtained by dividing the maximum power storage capacity by a rated capacity (i.e., a constant-multiple mathematical transformation).
Similarly, the accumulated amount of wear may include a constant-factor mathematical transformation of an index such as an accumulated charge amount, an accumulated discharge amount, and an accumulated charge-discharge amount. For example, in some cases, one integrated charge coefficient may be obtained by dividing the integrated charge amount by the rated capacity of the rechargeable battery, and the integrated charge coefficient may be used as the integrated wear amount. The definition of constant times mathematical transformation relating to both the accumulated discharge amount and the accumulated charge-discharge amount is also similar.
The decrement of the maximum stored energy capacity represents the decrement of the maximum stored energy capacity obtained as compared with the case where the rechargeable battery is just put into use. The method for obtaining the maximum power storage capacity fading condition includes an absolute fading quantity obtained by subtracting the current maximum power storage capacity from the maximum power storage capacity in the initial state, and also includes an absolute fading quantity obtained by subtracting the current maximum power storage capacity from the rated capacity, which is not limited in this application. Here, the attenuation of the maximum stored energy amount includes an absolute attenuation of the maximum stored energy amount, and also includes a relative attenuation rate (i.e., constant-multiple mathematical transformation) obtained by dividing the absolute attenuation by the rated capacity.
In addition, the internal resistance of the secondary battery also changes during use, and the amount of change in the internal resistance of the secondary battery indicates a change in the internal resistance of the secondary battery compared to when the secondary battery is just put into use. The change of the internal resistance of the battery includes the absolute change of the resistance and also includes the change rate obtained by dividing the absolute change by the initial resistance (namely, constant times mathematical transformation). For some power consuming devices, the amount of work accumulated or generated during the use of the rechargeable battery can be very convenient to measure and obtain. For example, the maximum storage capacity of the rechargeable battery can be used for the workload generated by the operation of the power consumption equipment, and the maximum storage capacity of the rechargeable battery can be used for the mileage generated by the driving of the automobile. The rechargeable battery supplies the accumulated work load of the normal operation of the power consumption equipment and the accumulated mileage of the normal running of the automobile. The index is directly related to the performance of the rechargeable battery, and therefore, the index can be used as a key performance index or can be accumulated to obtain an accumulated loss amount. The index is also applicable to the relevant definitions such as the rated index and constant times of mathematical transformation.
A model obtaining module 205 configured to obtain a degradation model of the rechargeable battery, for example, execute step 105.
And a residual life prediction module 207 configured to predict an estimated value of the residual life of the rechargeable battery according to the current SOH, the current life, the degradation model and a preset failure index, for example, execute step 107.
In some embodiments, the model obtaining module obtains a function of a degradation model of the rechargeable battery, including obtaining a preset degradation model; acquiring historical use data of the rechargeable battery and constructing a degradation model; acquiring historical use data of other rechargeable batteries of the same type and constructing a degradation model;
the failure index is preset as a certain value in the SOH value range of the rechargeable battery in advance, and when the SOH reaches the index, the rechargeable battery fails;
the residual life is a residual accumulative amount of the accumulative consumption, namely the accumulative consumption which can be additionally accumulated in a range from the forecast starting time to the time when the rechargeable battery is failed;
the current life is a current value of the accumulated amount of wear, that is, an accumulated amount of wear accumulated in a range from the time when the rechargeable battery is put into use to the prediction start time.
For a rechargeable battery, the degradation model may be set in advance, and thus may be directly obtained. Meanwhile, the degradation rule can be estimated from the historical use data of the rechargeable battery, so that a degradation model can be constructed in real time according to the historical data before the prediction process is started. Besides, the degradation model can be constructed by acquiring historical use data of other rechargeable batteries of the same type. For example, the data is collected by performing charge and discharge tests on the same type of rechargeable batteries, or the use data of the same type of rechargeable batteries used by other users is collected. The same type of rechargeable batteries herein includes rechargeable batteries of the same type, and also rechargeable batteries of the same manufacturing process and material.
The failure indicator may be a predetermined limit, for example, a value in the range of the SOH value of the rechargeable battery. The remaining life is a remaining accumulative amount of the accumulative consumption, for example, an accumulative consumption which can be additionally accumulated in a range from the prediction start time to the time when the rechargeable battery is failed. For example, the accumulated charge amount is set as the current life, and the preset failure index is 20% of the initial capacity. For a rechargeable battery with a rated capacity of 1000Mah, the failure limit is 200 Mah. After a long period of use, the cumulative amount of charge accumulated at present is 10000Mah, and the maximum current storage capacity is 600Mah, that is, the decrement in the maximum current storage capacity is 400 Mah. In this case, the maximum cumulative capacity of the rechargeable battery is again attenuated by 400Mah, and the failure limit of 200Mah is reached. Based on a simple linear mathematical model and according to the historical usage data of the battery, if the battery is to be attenuated by 400Mah, an additional cumulative charge of 10000Mah is still required, and thus the remaining usable life of the battery is 10000 Mah.
In some embodiments, the system further comprises a total life prediction module configured to obtain an estimated value of the total life of the rechargeable battery according to the current SOH, the current life, a degradation model and a preset failure index;
the SOH prediction module is configured to obtain corresponding SOH within the residual service life range according to the current SOH, the current service life, the degradation model and a preset failure index;
the system also comprises a planning module which is configured to output the planned replacement time according to at least one of seven indexes, such as the current SOH, the current service life, a degradation model, a preset failure index, the remaining service life, the total service life and the corresponding SOH in the remaining service life range;
the total life includes an accumulated amount of wear within a range from when the rechargeable battery is put into use to when the SOH reaches a failure index;
the residual life of the rechargeable battery also comprises a ratio of the residual accumulative amount of the accumulative consumption to the total life, namely the ratio of the accumulative consumption which can be additionally accumulated to the total life in a range from the forecast starting time to the time when the rechargeable battery is invalid;
the SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order.
For the rechargeable battery, degradation will occur continuously since the rechargeable battery is put into use, and when the SOH value reaches a preset failure index, the corresponding accumulated loss amount can be regarded as the total life, i.e. the accumulated loss amount in the range from the time the rechargeable battery is put into use to the time the SOH reaches the failure index. In addition, the remaining life may include the remaining life in a relative sense, i.e., the ratio of the remaining accumulative amount of the accumulative wear amount to the total life. For example, 30% of the cumulative amount of wear that can be accumulated additionally remains.
The accumulated amount of wear will increase continuously as the rechargeable battery is used continuously, so the present invention uses the accumulated amount of wear as the life index. In the future, as long as the rechargeable battery has not failed, the rechargeable battery can be used continuously, and therefore the accumulated amount of wear continues to accumulate. The SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order. The description of "at least one" is used herein and thus includes any one or more of the corresponding SOHs over the remaining life span. The planned replacement time is output to remind a user in time before the battery fails. For example, when the acquired remaining life is insufficient, the user needs to be reminded to replace the rechargeable battery. Alternatively, the user may be informed of the ideal battery replacement time by calculating it in advance.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It should be noted that, in the present application, there is no strict sequential execution order among the steps, and as long as a logical order is met, the steps may be executed simultaneously or according to a certain preset order, and fig. 1 to fig. 2 are only schematic manners, and do not represent only such an execution order.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for predicting the service life of a rechargeable battery based on accumulated loss is characterized by comprising the following steps:
(1) acquiring or estimating the accumulated consumption of the rechargeable battery as the current life;
(2) obtaining or estimating a key performance index of the rechargeable battery as a current SOH;
(3) acquiring a degradation model of the rechargeable battery;
(4) and predicting the estimated value of the residual life of the rechargeable battery according to the current SOH, the current life, the degradation model and a preset failure index.
2. The method of claim 1, wherein the accumulated amount of wear is used to predict the life of the rechargeable battery,
the accumulated loss amount comprises at least one of an accumulated charge amount, an accumulated discharge amount, an accumulated charge and discharge amount and the like;
the accumulated loss amount comprises at least one constant multiple mathematical transformation of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the key performance index comprises at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the key performance indexes comprise constant multiple mathematical transformation of at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like.
3. The method of claim 2, wherein the accumulated amount of wear is used to predict the life of the rechargeable battery,
the accumulated consumption comprises at least one of accumulated working capacity of the rechargeable battery for normal operation of power consumption equipment, accumulated mileage of the rechargeable battery for normal running of the automobile and the like;
the accumulated loss amount comprises at least one constant multiple mathematical transformation of accumulated working amount of the rechargeable battery for normal operation of power consumption equipment, accumulated mileage amount of the rechargeable battery for normal running of the automobile and the like;
the key performance indexes comprise at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like;
the key performance indexes comprise constant multiple mathematical transformation of at least one of the maximum power storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum power storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like.
4. A method according to any one of claims 2-3, wherein the step of obtaining a degradation model of the rechargeable battery comprises:
acquiring a preset degradation model;
acquiring historical use data of the rechargeable battery and constructing a degradation model;
and acquiring historical use data of other rechargeable batteries of the same type and constructing a degradation model.
5. The method of claim 4, wherein the accumulated amount of wear is used to predict the life of the rechargeable battery,
the failure index is preset as a certain value in the SOH value range of the rechargeable battery in advance, and when the SOH reaches the index, the rechargeable battery fails;
the residual life is a residual accumulative amount of the accumulative consumption, namely the accumulative consumption which can be additionally accumulated in a range from the forecast starting time to the time when the rechargeable battery is failed;
the current life is a current value of the accumulated amount of wear, that is, an accumulated amount of wear accumulated in a range from the time when the rechargeable battery is put into use to the prediction start time.
6. The method of claim 5 wherein the accumulated amount of wear-based battery life prediction is performed,
obtaining an estimated value of the total service life of the rechargeable battery according to the current SOH, the current service life, a degradation model and a preset failure index;
obtaining a corresponding SOH within a remaining life range according to a current SOH, a current life, a degradation model and a preset failure index;
outputting planned replacement time according to at least one of seven indexes, such as current SOH, current service life, a degradation model, preset failure indexes, residual service life, total service life and corresponding SOH in a residual service life range;
the total life includes an accumulated amount of wear within a range from when the rechargeable battery is put into use to when the SOH reaches a failure index;
the residual life of the rechargeable battery also comprises the ratio of the residual accumulative amount of the accumulative consumption to the total life, namely the ratio of the accumulative consumption which can be additionally accumulated to the total life in the range from the prediction starting time to the time when the rechargeable battery is invalid;
the SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order.
7. A rechargeable battery life prediction apparatus based on an accumulated amount of wear, comprising:
an accumulated amount of wear acquisition module configured to acquire or estimate an accumulated amount of wear of the rechargeable battery as a current life;
a key performance index acquisition module configured to acquire or estimate a key performance index of the rechargeable battery as a current SOH;
a model acquisition module configured to acquire a degradation model of the rechargeable battery;
and the residual life prediction module is configured to predict the estimated value of the residual life of the rechargeable battery according to the current SOH, the current life, the degradation model and a preset failure index.
8. The device of claim 7, wherein the accumulated amount of wear-based battery life prediction unit,
the accumulated amount of wear obtained by the accumulated amount of wear obtaining module comprises at least one of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the accumulated loss amount obtained by the accumulated loss amount obtaining module comprises constant times mathematical transformation of at least one of accumulated charge amount, accumulated discharge amount, accumulated charge and discharge amount and the like;
the key performance index acquired by the key performance index acquisition module comprises at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the key performance index acquired by the key performance index acquisition module comprises constant multiple mathematical transformation of at least one of maximum power storage capacity, attenuation of the maximum power storage capacity, internal resistance of the battery, variation of the internal resistance of the battery and the like;
the accumulated consumption acquired by the accumulated consumption acquisition module comprises at least one of accumulated working capacity accumulated by the normal operation of the rechargeable battery for the power consumption equipment, accumulated mileage accumulated by the normal running of the rechargeable battery for the automobile and the like;
the accumulated consumption obtained by the accumulated consumption obtaining module comprises constant times mathematical transformation of at least one of accumulated working capacity of the rechargeable battery for normal operation of the power consumption equipment, accumulated mileage of the rechargeable battery for normal running of the automobile and the like;
the key performance index acquired by the key performance index acquisition module comprises at least one of the maximum electricity storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum electricity storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like;
the key performance index acquired by the key performance index acquisition module comprises constant times mathematical transformation of at least one of the maximum power storage capacity of the rechargeable battery for the workload generated by the running of power consumption equipment, the maximum power storage capacity of the rechargeable battery for the mileage generated by the running of an automobile and the like.
9. The apparatus of claim 8, wherein the accumulated amount of wear-based rechargeable battery life prediction unit,
the model obtaining module obtains the functions of a degradation model of the rechargeable battery, including obtaining a preset degradation model; acquiring historical use data of the rechargeable battery and constructing a degradation model; acquiring historical use data of other rechargeable batteries of the same type and constructing a degradation model;
the failure index is preset as a certain value in the SOH value range of the rechargeable battery in advance, and when the SOH reaches the index, the rechargeable battery fails;
the residual life is a residual accumulative amount of the accumulative consumption, namely the accumulative consumption which can be additionally accumulated in a range from the forecast starting time to the time when the rechargeable battery is failed;
the current life is a current value of the accumulated amount of wear, that is, an accumulated amount of wear accumulated in a range from the time when the rechargeable battery is put into use to the prediction start time.
10. The apparatus of claim 9, wherein the accumulated amount of wear-based rechargeable battery life prediction unit,
the total service life prediction module is configured to obtain an estimated value of the total service life of the rechargeable battery according to the current SOH, the current service life, the degradation model and a preset failure index;
the SOH prediction module is configured to obtain the corresponding SOH within the remaining life range according to the current SOH, the current life, the degradation model and a preset failure index;
the system also comprises a planning module which is configured to output the planned replacement time according to at least one of seven indexes, such as the current SOH, the current service life, a degradation model, a preset failure index, the remaining service life, the total service life and the corresponding SOH in the remaining service life range;
the total life includes an accumulated amount of wear within a range from when the rechargeable battery is put into use to when the SOH reaches a failure index;
the residual life of the rechargeable battery also comprises the ratio of the residual accumulative amount of the accumulative consumption to the total life, namely the ratio of the accumulative consumption which can be additionally accumulated to the total life in the range from the prediction starting time to the time when the rechargeable battery is invalid;
the SOH corresponding to the remaining life range includes at least one of SOHs corresponding to a range from the predicted start time to a time when the rechargeable battery is out of order.
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| PCT/CN2022/097748 WO2023284453A1 (en) | 2021-07-15 | 2022-06-09 | Cumulative consumption-based rechargeable battery life prediction method and apparatus, electronic device, and readable storage medium |
| US18/163,357 US20240054269A1 (en) | 2021-07-15 | 2023-02-02 | Method, device, electronic equipment and computer-readable storage medium for lifetime prognosis of rechargeable-battery based on cumulative-consumption-indicators |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023284453A1 (en) * | 2021-07-15 | 2023-01-19 | 崔跃芹 | Cumulative consumption-based rechargeable battery life prediction method and apparatus, electronic device, and readable storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06163084A (en) * | 1992-09-29 | 1994-06-10 | Yuasa Corp | Measuring method for residual life of electric motor car |
| CN105445671A (en) * | 2015-12-29 | 2016-03-30 | 北京航天测控技术有限公司 | Lithium ion battery service life prediction method based on traceless particle filtering |
| CN107179512A (en) * | 2017-07-12 | 2017-09-19 | 万帮充电设备有限公司 | Predict the method and device of battery life |
| CN108490365A (en) * | 2018-04-18 | 2018-09-04 | 北京理工大学 | A method of the remaining life of the power battery of estimation electric vehicle |
| CN109061504A (en) * | 2018-08-28 | 2018-12-21 | 中北大学 | Same type difference lithium ion battery remaining life prediction technique and system |
| CN109975713A (en) * | 2019-04-12 | 2019-07-05 | 苏州正力蔚来新能源科技有限公司 | A kind of power battery SOH estimation method considering multifactor impact |
| CN110008235A (en) * | 2019-04-15 | 2019-07-12 | 优必爱信息技术（北京）有限公司 | Power battery health degree evaluation method, apparatus and system |
| CN111446514A (en) * | 2020-03-17 | 2020-07-24 | 上海理工大学 | Method for reasonably adjusting discharge cut-off voltage of lithium battery along with service life attenuation of battery |
| CN112277729A (en) * | 2019-12-31 | 2021-01-29 | 蜂巢能源科技有限公司 | Method and device for predicting total driving mileage of electric vehicle |
| CN112595980A (en) * | 2020-12-17 | 2021-04-02 | 北京海博思创科技股份有限公司 | Method, device and equipment for predicting service life of battery energy storage system |
| CN112736268A (en) * | 2020-12-17 | 2021-04-30 | 华中科技大学 | Control optimization method and system for prolonging service life of SOFC (solid oxide fuel cell) system |
-
2021
- 2021-07-15 CN CN202110798763.5A patent/CN114460484B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06163084A (en) * | 1992-09-29 | 1994-06-10 | Yuasa Corp | Measuring method for residual life of electric motor car |
| CN105445671A (en) * | 2015-12-29 | 2016-03-30 | 北京航天测控技术有限公司 | Lithium ion battery service life prediction method based on traceless particle filtering |
| CN107179512A (en) * | 2017-07-12 | 2017-09-19 | 万帮充电设备有限公司 | Predict the method and device of battery life |
| CN108490365A (en) * | 2018-04-18 | 2018-09-04 | 北京理工大学 | A method of the remaining life of the power battery of estimation electric vehicle |
| CN109061504A (en) * | 2018-08-28 | 2018-12-21 | 中北大学 | Same type difference lithium ion battery remaining life prediction technique and system |
| CN109975713A (en) * | 2019-04-12 | 2019-07-05 | 苏州正力蔚来新能源科技有限公司 | A kind of power battery SOH estimation method considering multifactor impact |
| CN110008235A (en) * | 2019-04-15 | 2019-07-12 | 优必爱信息技术（北京）有限公司 | Power battery health degree evaluation method, apparatus and system |
| CN112277729A (en) * | 2019-12-31 | 2021-01-29 | 蜂巢能源科技有限公司 | Method and device for predicting total driving mileage of electric vehicle |
| CN111446514A (en) * | 2020-03-17 | 2020-07-24 | 上海理工大学 | Method for reasonably adjusting discharge cut-off voltage of lithium battery along with service life attenuation of battery |
| CN112595980A (en) * | 2020-12-17 | 2021-04-02 | 北京海博思创科技股份有限公司 | Method, device and equipment for predicting service life of battery energy storage system |
| CN112736268A (en) * | 2020-12-17 | 2021-04-30 | 华中科技大学 | Control optimization method and system for prolonging service life of SOFC (solid oxide fuel cell) system |
Non-Patent Citations (4)
| Title |
|---|
| 曹孟达 等: "基于粒子群算法的卫星蓄电池区间预测方法", 无线电工程, vol. 50, no. 4, pages 286 * |
| 王春雷 等: "基于改进相关向量机的锂电池寿命预测方法", 北京航空航天大学学报, vol. 44, no. 9, pages 1998 * |
| 蒋洪湖: "基于伽马分布的锂离子电池容量退化建模", 《新能源汽车》 * |
| 蒋洪湖: "基于伽马分布的锂离子电池容量退化建模", 《新能源汽车》, no. 5, 15 March 2019 (2019-03-15), pages 1 - 3 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023284453A1 (en) * | 2021-07-15 | 2023-01-19 | 崔跃芹 | Cumulative consumption-based rechargeable battery life prediction method and apparatus, electronic device, and readable storage medium |
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