CN116953523A - Method and system for quantifying and predicting residual use value of power battery - Google Patents
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Abstract
The invention provides a method and a system for quantifying and predicting the residual use value of a power battery, wherein the scheme comprises the steps of obtaining battery health status, residual service life and capacity attenuation rate index data of each single battery; based on the index data, calculating the weight of each index by using an entropy weight method; determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight; determining the residual use value percentage of the single battery based on the initial score, the use value ratio influenced by the battery performance parameters and the battery raw material cost ratio; and obtaining the prediction result of the residual value of each single battery based on the residual use value percentage of the single battery and the price of the single battery.
Description
Technical Field
The invention belongs to the technical field of power batteries, and particularly relates to a method and a system for quantifying and predicting residual use value of a power battery.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The exhaust gas of the conventional fuel automobile contains carbon monoxide and nitrous oxide, which are harmful to the environment and cause global warming. In order to reduce energy loss and protect environment, new energy automobiles are generated. With the rapid increase in the amount of electric vehicles retained, the demand for lithium ion batteries has shown a tendency to increase in an explosive manner. At the end of 2022, the installed quantity of the global electric automobile battery reaches 517.9GWh, and the same ratio is increased by 71.8%. The decommissioning of lithium ion batteries also has a rapidly increasing trend, and it is expected that in 2023, the decommissioning of lithium ion power batteries will reach 48.09GWh.
The use of power cells must be accompanied by losses and attenuation. When the performance of the vehicle-mounted battery no longer meets the daily use requirement of the automobile, the vehicle-mounted battery needs to be replaced with a new battery in time. The remaining utility value of the battery is affected by the coupling of various parameters. The battery will gradually age during use, and its capacity, battery state of health SOH (state of health), remaining life RUL (Remaining Useful Life), etc. parameters will all change. The retired battery still has about 80% of capacity, and for different batteries, the battery capacity attenuation rate is different, so that a subsequent service life curve of the battery presents different trends, and even if the capacity attenuation and service life consumption are the same before the retirement, the residual value of the battery is different due to the different capacity attenuation rates, and the subsequent applicable fields are different. Therefore, the accurate and objective evaluation of the residual use value (Remaining useful Value, RUV) of the battery and the reasonable prediction of the residual value of the battery are of great significance for the recycling of the battery.
In order to better evaluate the remaining value of the battery, some techniques have been proposed. For example, the chinese patent CN 201610056431.9 calculates the remaining value of the battery according to the ratio of the remaining charge/discharge times of the battery to the completely new charge/discharge times. The method only considers one parameter of the battery RUL, does not consider other parameters affecting the battery performance, and has low evaluation accuracy. The Chinese patent CN 201910106919.1 calculates the value of the retired battery by adding the products of the preset weights and the indexes to obtain quantitative evaluation indexes. However, the method for setting the weight is subjective weighting, and has poor objectivity; the calculation method of the quantitative evaluation index is rough and has low accuracy.
In conclusion, as the factors influencing the residual value of the battery are more and are mutually coupled, the residual value of the battery is not measured by unified and objective indexes in the recovery process of the battery, the existing scheme cannot obtain more accurate residual use value according to the performance parameters of the battery, and the existing scheme is neither objective nor accurate.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for quantifying and predicting the residual use value of a power battery, wherein the scheme takes parameters affecting the battery performance such as the battery health state, the residual service life, the capacity attenuation rate and the like as parameters, defines the residual value of the battery, fully considers various parameters affecting the battery performance, avoids the problem of inaccurate estimation caused by single factors, and has objective and accurate result; meanwhile, the scheme utilizes the objective weighting method to weight each index, and has the characteristics of high accuracy and strong robustness compared with the subjective weighting method.
According to a first aspect of the embodiment of the present invention, there is provided a method for quantifying and predicting remaining use value of a power battery, including:
acquiring battery health state, residual service life and capacity attenuation rate index data of each single battery;
based on the index data, calculating the weight of each index by using an entropy weight method;
determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight;
determining the residual use value percentage of the single battery based on the initial score, the use value ratio influenced by the battery performance parameters and the battery raw material cost ratio;
and obtaining the prediction result of the residual value of each single battery based on the residual use value percentage of the single battery and the price of the single battery.
Further, the calculating of the weight value of each index based on the index data by using an entropy weight method specifically comprises the following steps:
acquiring the duty ratio of each sample value under each index;
calculating the information entropy value of each index based on the duty ratio of each sample value;
and calculating the weight corresponding to each index based on the information entropy value corresponding to each index.
Further, the calculation of the information entropy value is specifically expressed as follows:
wherein q ij The index proportion of the ith sample under the jth index is represented by m, the index number is represented by m, and the number of the single batteries to be detected is represented by n.
Further, the weight corresponding to the index is specifically expressed as follows:
wherein M is j The information entropy value of the j index.
Further, the determining the initial score of the single battery based on the maximum and minimum values of the index data and the weight of the index specifically includes:
obtaining a maximum value and a minimum value in corresponding sample values under each index, and obtaining a maximum value set and a minimum value set;
respectively calculating the distance between each index value corresponding to each single battery to be predicted and the maximum value set and the minimum value set to obtain a first distance and a second distance;
calculating the ratio of the second distance relative to the sum of the first distance and the second distance as the initial score of the single battery;
or, the initial score of the single battery specifically adopts the following formula:
wherein Z is j + For the maximum value corresponding to the jth index, Z j - Is the minimum value, omega corresponding to the j-th index j For the weight value corresponding to the jth index, z ij The index value of the j item corresponding to the i-th single battery is obtained.
Further, the method determines the remaining usage value percentage of the single battery based on the initial score, the usage value ratio affected by the battery performance parameter and the battery raw material cost ratio, and specifically comprises the following steps:
Per＝T i *90％+10％
wherein T is i For the initial score of the ith single cell, 90% is the usage value duty ratio of the influence of the cell performance parameter, and 10% is the cell raw material cost duty ratio.
Further, the product of the residual use value percentage of the single battery and the price of the single battery is used as a prediction result of the residual value of the single battery.
According to a second aspect of the embodiment of the present invention, there is provided a power battery remaining use value quantifying and predicting system, including:
the data acquisition unit is used for acquiring battery health state, residual service life and capacity attenuation rate index data of each single battery;
a weight calculation unit for calculating a weight for each index using an entropy weight method based on the index data;
an initial score acquisition unit for determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight;
a remaining use value duty ratio acquisition unit for determining a percentage of remaining use value of the single battery based on the initial score, the use value duty ratio affected by the battery performance parameter, and the battery raw material cost duty ratio;
and a remaining use value prediction unit for obtaining a prediction result of the remaining value of each unit cell based on the percentage of the remaining use value of the unit cell and the price of the unit cell.
According to a third aspect of the embodiment of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored to run on the memory, where the processor implements the method for quantifying and predicting remaining use value of a power battery when executing the program.
According to a fourth aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the described method of quantifying and predicting remaining use value of a power battery.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention provides a method and a system for quantifying and predicting the residual use value of a power battery, wherein the scheme takes parameters which influence the battery performance, such as the battery health state, the residual service life, the capacity attenuation rate and the like, as parameters, so as to define the residual value of the battery, fully consider various parameters which influence the battery performance, avoid the problem of inaccurate estimation caused by single factors and have objective and accurate results;
(2) The scheme of the invention utilizes the objective weighting method to weight each index, and has the characteristics of high accuracy and strong robustness compared with the subjective weighting method;
(3) According to the invention, the scores of the indexes are calculated according to the maximum and minimum distance method, the calculation method is simple, convenient, efficient and accurate, the time can be saved in the actual recycling process, and the practicability is higher;
(4) The invention predicts the subsequent residual use value according to the existing data by using the prediction method, has accurate prediction result and is beneficial to reasonable recycling.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flowchart of a method for quantifying and predicting the remaining utilization value of a power battery according to an embodiment of the present invention;
FIG. 2 is a cyclic graph of pool capacity as described in an embodiment of the present invention;
fig. 3 is a graph of the remaining value percentage of the battery according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Embodiment one:
the purpose of this embodiment is to provide a method for quantifying and predicting the remaining use value of a power battery.
A method for quantifying and predicting the residual use value of a power battery comprises the following steps:
acquiring battery health state, residual service life and capacity attenuation rate index data of each single battery;
based on the index data, calculating the weight of each index by using an entropy weight method;
determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight;
determining the residual use value percentage of the single battery based on the initial score, the use value ratio influenced by the battery performance parameters and the battery raw material cost ratio;
and obtaining the prediction result of the residual value of each single battery based on the residual use value percentage of the single battery and the price of the single battery.
In a specific implementation, the calculating of the weight of each index based on the index data by using an entropy weight method specifically includes:
acquiring the duty ratio of each sample value under each index;
calculating the information entropy value of each index based on the duty ratio of each sample value;
and calculating the weight corresponding to each index based on the information entropy value corresponding to each index.
The calculation of the information entropy value is specifically expressed as follows:
wherein q ij The index proportion of the ith sample under the jth index is represented by m, the index number is represented by m, and the number of the single batteries to be detected is represented by n.
The weight corresponding to the index is specifically expressed as follows:
wherein M is j The information entropy value of the j index.
In a specific implementation, the determining the initial score of the single battery based on the maximum and minimum values of each index data and each index weight specifically includes:
obtaining a maximum value and a minimum value in corresponding sample values under each index, and obtaining a maximum value set and a minimum value set;
respectively calculating the distance between each index value corresponding to each single battery to be predicted and the maximum value set and the minimum value set to obtain a first distance and a second distance;
and calculating the ratio of the second distance relative to the sum of the first distance and the second distance as the initial score of the single battery.
In a specific implementation, the method determines the remaining use value percentage of the single battery based on the initial score, the use value ratio affected by the battery performance parameter and the battery raw material cost ratio, and specifically includes the following steps:
Per＝T i *90％+10％
wherein T is i For the initial score of the ith single cell, 90% is the usage value duty ratio of the influence of the cell performance parameter, and 10% is the cell raw material cost duty ratio.
In the specific implementation, the product of the residual use value percentage of the single battery and the price of the single battery is taken as the prediction result of the residual value of the single battery.
In particular, for easy understanding, the following detailed description of the embodiments will be given with reference to the accompanying drawings:
the scheme of the embodiment provides a method for quantifying and predicting the residual use value of a power battery, wherein parameters such as SOH, RUL, capacity attenuation rate and the like affecting the performance of the battery are taken as parameters, the entropy weight method is utilized for weighting, the distance between an evaluation index and the maximum and minimum values is calculated, the score of each evaluation index is obtained, and the residual use value of the battery is calculated according to the score.
The scheme in this embodiment is based on the following criteria in parameter selection affecting the battery performance:
the battery SOH changes between 0 and 100%, the total RUL of the battery is the cycle number of the battery SOH from 100% to 20%, the capacity cycle curves of the battery are shown in fig. 2, it can be seen that the capacity change amount of the battery 1 and the battery 2 in the process from the point A to the point B is the same, the SOH changes consistently, and the battery is the same battery, so the residual life RUL is the same. However, the slopes of the two curves at the point B are different, the change rates of the capacities are different, the capacity of the battery 1 with larger change rate of the capacities can be quickly reduced, the battery 1 can reach a complete scrapping state more quickly, the residual use value is lower, the capacity curve of the battery 2 with smaller change rate of the capacities is slower, the continuous use time is longer, and the residual use value is higher. Therefore, the capacity reduction rate is also an important parameter affecting the remaining use value of the battery, and thus RUV is defined by selecting indexes such as SOH, RUL, and capacity reduction rate.
In the specific implementation, three indexes of SOH, RUL and capacity attenuation rate are weighted by an entropy weight method, wherein the entropy weight method is a method for objectively determining weights, and has certain accuracy compared with subjective methods such as an analytic hierarchy process and the like. Secondly, the weight determined by the method can be corrected, so that the characteristic of higher adaptability is determined, and the entropy weight method comprises the following steps:
step 1, determining an evaluation index, constructing an evaluation index system and constructing a horizontal matrix Y
Wherein y is 11 ,y 21 …y n1 For each value of the first index, y 1m ,y 2m …y nm And n is the number of the single batteries to be detected, and m is the index number.
Step 2, carrying out standardization processing on the evaluation matrix to obtain a matrix R, wherein positive and negative indexes are unified into positive indexes to obtain a standardized matrix
Wherein z is 11 ,z 12 …z nm Is the index value after forward conversion.
Step 3, normalizing and calculating the proportion of the ith sample value to the index under the jth index
Wherein z is ij Is the index value in the formula (2).
Step 4, calculating the index information entropy value M j
Wherein q ij The index specific gravity is the sample value obtained by the formula (3).
Step 5, calculating the weight omega of each index j
Wherein M is j Entropy value of each index information.
The weights of the various indexes are shown in table 1:
TABLE 1 weight of each index
| SOH | RUL | … | Capacity decay rate | |
| Weighting of | ω s | ω r | … | ω o |
In a specific implementation, the scoring expression is derived according to the comprehensive evaluation method in combination with the weight expression, and the comprehensive evaluation method adopted in the embodiment is a maximum-minimum method, which can make full use of the original data information, and the result can accurately reflect the difference between the evaluation schemes.
Specifically, the maximum and minimum method comprises the following steps:
step 1, defining a maximum value (namely a maximum value set):
wherein z is 11 ,z 21 …z n1 Is the value of each index in formula (2) after forward conversion, z 1m ,z 2m …z nm And the same is true.
Step 2, defining a minimum value (namely a minimum value set):
step 3, defining the distance (i=1, 2, … …, n) between the ith unit cell to be predicted and the maximum value (namely, the first distance):
in the method, in the process of the invention,is the maximum value defined in formula (6), ω j The weight obtained by the formula (5).
Step 4, defining the distance between the ith (i=1, 2, … …, n) to be predicted single battery and the minimum value (namely, the second distance):
in the method, in the process of the invention,is the minimum value defined in formula (7).
Step 5, calculating the unnormalized score of the ith single battery to be predicted as follows:
substituting formula (8) and formula (9) into formula (10) yields formula (11):
obtaining a calculation formula of the remaining use value percentage of the battery according to the battery score of the formula (11):
Per＝T i *90％+10％ (12)
wherein, the use value of the performance parameter effect accounts for 90 percent of the value percentage, and the battery raw material cost accounts for 10 percent of the value percentage.
The calculation formula of the battery residual value RUV is:
RUV＝P*Per i (13)
in Per i And scoring each single battery, wherein P is the market price of the single battery, and the total residual value of the battery pack is calculated according to the coupling of the residual values of the single batteries.
Further, to demonstrate the effectiveness of the protocol described in this example, the following experiments were performed:
according to the data construction embodiment in the MIT data set, the capacity data of each constant-current charging cycle of the battery is selected to calculate SOH, and the residual service life RUL of the battery is calculated by predicting the service life of the battery. The 124 battery data are used to simulate the battery cell data in the battery pack of a new energy automobile.
The weights of the indexes obtained according to the entropy weight method are shown in table 1:
TABLE 1 weight of SOH, RUL, number of overcharging and overdischarging
| SOH | RUL | Capacity decay rate | |
| Weighting of | 0.39 | 0.33 | 0.28 |
The indexes and the residual value percentages of the partial vehicle-mounted lithium ion battery monomers are shown in the table 2:
TABLE 2 partial cell index and residual value percentage
| Sequence number | SOH(％) | RUL (times) | Capacity decay rate | Residual value (%) |
| 1 | 98 | 3857 | 0.0003 | 98.6 |
| 2 | 97 | 2532 | 0.0004 | 87.2 |
| 3 | 95 | 992 | 0.0011 | 73.3 |
| 4 | 86 | 662 | 0.0002 | 43.6 |
| 5 | 98 | 3862 | 0.0004 | 95.7 |
| 6 | 87 | 556 | 0.0004 | 42.9 |
| 7 | 89 | 884 | 0.0004 | 52.2 |
| 8 | 90 | 859 | 0.0004 | 51.6 |
| 9 | 92 | 517 | 0.0013 | 40.3 |
| 10 | 96 | 3344 | 0.0003 | 86.4 |
The percentage of remaining value of each battery is plotted according to the capacity cycle data of 124 battery cells as shown in fig. 3:
as can be seen from table 2 and fig. 3, the lower the SOH of the battery, the smaller the RUL, the greater the capacity fade rate, the smaller the subsequent use value of the battery, and the lower the remaining value percentage. The SOH and RUL of the No. 1 battery and the No. 5 battery are basically the same, but the capacity attenuation rate of the No. 5 battery is larger, the residual value percentage is lower, and the correctness and rationality of the scoring method are illustrated.
The remaining usage value RUV of each battery can be calculated according to the remaining value percentage of each battery and the calculation formula in formula (13), and the unit price and the raw material price of the battery vary with the market, so the unit price setting in this embodiment is only one assumed value, and can be set to an appropriate value according to the market variation.
Embodiment two:
an object of the present embodiment is to provide a system for quantifying and predicting remaining use value of a power battery.
A power cell remaining use value quantification and prediction system, comprising:
the data acquisition unit is used for acquiring battery health state, residual service life and capacity attenuation rate index data of each single battery;
a weight calculation unit for calculating a weight for each index using an entropy weight method based on the index data;
an initial score acquisition unit for determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight;
a remaining use value duty ratio acquisition unit for determining a percentage of remaining use value of the single battery based on the initial score, the use value duty ratio affected by the battery performance parameter, and the battery raw material cost duty ratio;
and a remaining use value prediction unit for obtaining a prediction result of the remaining value of each unit cell based on the percentage of the remaining use value of the unit cell and the price of the unit cell.
Further, the related technical details of the system in this embodiment are described in the first embodiment, so they are not described herein.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of embodiment one. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented as a hardware processor executing or implemented by a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. 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.
The method and the system for quantifying and predicting the residual use value of the power battery provided by the embodiment can be realized, and have wide application prospects.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility value quantization and prediction method for the power battery is characterized by comprising the following steps:
acquiring battery health state, residual service life and capacity attenuation rate index data of each single battery;
based on the index data, calculating the weight of each index by using an entropy weight method;
determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight;
determining the residual use value percentage of the single battery based on the initial score, the use value ratio influenced by the battery performance parameters and the battery raw material cost ratio;
and obtaining the prediction result of the residual value of each single battery based on the residual use value percentage of the single battery and the price of the single battery.
2. The method for quantifying and predicting remaining use value of a power battery according to claim 1, wherein the calculating a weight for each index based on the index data by using an entropy weight method comprises:
acquiring the duty ratio of each sample value under each index;
calculating the information entropy value of each index based on the duty ratio of each sample value;
and calculating the weight corresponding to each index based on the information entropy value corresponding to each index.
3. The method for quantifying and predicting remaining utilization value of a power battery according to claim 2, wherein the calculation of the entropy value of information is specifically represented by:
wherein q ij The index proportion of the ith sample under the jth index is represented by m, the index number is represented by m, and the number of the single batteries to be detected is represented by n.
4. The method for quantifying and predicting the residual utilization value of a power battery according to claim 2, wherein the weight corresponding to the index is specifically expressed as follows:
wherein M is j The information entropy value of the j index.
5. The method for quantifying and predicting remaining use value of a power battery according to claim 1, wherein the determining an initial score of a single battery based on a maximum and minimum value of each index data and each index weight is specifically:
obtaining a maximum value and a minimum value in corresponding sample values under each index, and obtaining a maximum value set and a minimum value set;
respectively calculating the distance between each index value corresponding to each single battery to be predicted and the maximum value set and the minimum value set to obtain a first distance and a second distance;
calculating the ratio of the second distance relative to the sum of the first distance and the second distance as the initial score of the single battery;
or, the initial score of the single battery specifically adopts the following formula:
wherein Z is j + Is the j index pairMaximum value of the stress, Z j - Is the minimum value, omega corresponding to the j-th index j For the weight value corresponding to the jth index, z ij The index value of the j item corresponding to the i-th single battery is obtained.
6. The method for quantifying and predicting the remaining use value of a power battery according to claim 1, wherein the determining the percentage of the remaining use value of the single battery based on the initial score, the use value duty ratio affected by the battery performance parameter, and the battery raw material cost duty ratio is specifically shown as follows:
Per＝T i *90％+10％
wherein T is i For the initial score of the ith single cell, 90% is the usage value duty ratio of the influence of the cell performance parameter, and 10% is the cell raw material cost duty ratio.
7. The method for quantifying and predicting residual battery life of claim 1, wherein the product of the percentage of residual battery life and the price of the battery is used as the residual battery life prediction result.
8. A system for quantifying and predicting the remaining use value of a power battery, comprising:
the data acquisition unit is used for acquiring battery health state, residual service life and capacity attenuation rate index data of each single battery;
a weight calculation unit for calculating a weight for each index using an entropy weight method based on the index data;
an initial score acquisition unit for determining an initial score of the single battery based on the maximum and minimum values of each index data and each index weight;
a remaining use value duty ratio acquisition unit for determining a percentage of remaining use value of the single battery based on the initial score, the use value duty ratio affected by the battery performance parameter, and the battery raw material cost duty ratio;
and a remaining use value prediction unit for obtaining a prediction result of the remaining value of each unit cell based on the percentage of the remaining use value of the unit cell and the price of the unit cell.
9. An electronic device comprising a memory, a processor and a computer program stored for execution on the memory, wherein the processor, when executing the program, implements a method for quantifying and predicting the remaining use value of a power battery as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of quantifying and predicting remaining use value of a power cell as claimed in any one of claims 1 to 7.
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