CN108919129A - When a kind of under variable working condition power battery life-span prediction method - Google Patents
When a kind of under variable working condition power battery life-span prediction method Download PDFInfo
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Abstract
本发明提供了一种时变工况下动力电池的寿命预测方法，该方法包括：基于车用动力电池的实际工况，以影响动力电池寿命的三个主要因素T、C、DOD为变量，结合车用工况下影响因素的时变性，首先在单次循环的时间尺度内，建立时变电流工况动力电池的寿命预测模型，再在多次循环的时间尺度内，考虑运行温度和放电深度DOD对电池寿命的影响，最终得到更接近实际使用工况的时变工况动力电池的寿命模型，提高了车用动力电池寿命预测的准确度，可以通过该模型计算出的电池寿命指导车用动力电池的使用、维护及更换，确保了电动车的安全使用。此外，该模型简单易于计算，有利于提高预测效率。
The invention provides a method for predicting the life of a power battery under time-varying working conditions. The method includes: based on the actual working conditions of a power battery for a vehicle, taking three main factors T, C, and DOD that affect the life of the power battery as variables, Combined with the time-varying nature of influencing factors under vehicle operating conditions, firstly, within the time scale of a single cycle, a life prediction model for power batteries under time-varying current conditions is established, and then within the time scale of multiple cycles, the operating temperature and discharge The impact of deep DOD on battery life finally obtains a time-varying power battery life model that is closer to the actual use condition, which improves the accuracy of vehicle power battery life prediction. The battery life calculated by this model can guide the vehicle The use, maintenance and replacement of power batteries ensure the safe use of electric vehicles. In addition, the model is simple and easy to calculate, which is conducive to improving the prediction efficiency.
Description
Technical field
The present invention relates to power battery technology field, in particular to it is a kind of when variable working condition under power battery service life
Prediction technique.
Background technique
In recent years, each state is all actively developing research new-energy automobile, and lithium ion battery is big with energy density, work electricity
Pressure is high, has extended cycle life, the features such as self-discharge rate is low and memory-less effect, the application as drive energy in power battery field
It is more and more.
The development process of lithium-ion-power cell includes electrical property, core function, service life and safety etc., service life exploitation
It is the most important thing.In use, the stress type for influencing its aging is more, including environment temperature, humidity, machinery pressure for battery
Power, radiation, electric current, voltage, SOC range etc..In many factors, the stress mainly influenced that has on cell degradation is environment temperature
And (multiplying power, DOD) in its use process.In use, the capacity of battery and internal resistance can change, battery capacity
The service life of decaying or the increased rule of internal resistance commonly used to characterize and predict battery.
In order to obtain battery life data, lithium ion battery ageing research majority is based on carrying out under the operating condition of laboratory, stress
Keep constant at any time, such as under a certain set temperature, constant current constant current-constant pressure charge and discharge, establish life model, parsing is answered
The relationship of power and lifetime change.However automobile-used operating condition alternation is various, temperature, electric current usually change over time, especially electric current, very
To being quickly to change, therefore can not directly predict that the battery under actual condition is old based on the life model established under the operating condition of laboratory
Change behavior.
Summary of the invention
In consideration of it, the invention proposes it is a kind of when variable working condition under power battery life-span prediction method, it is intended to solve existing
Power battery Life Prediction Model does not meet the problem for actually using operating condition and causing prediction result accuracy not high.
The invention proposes it is a kind of when variable working condition under power battery life-span prediction method, include the following steps:Step S1,
Based on single cycle, the Life Prediction Model of power battery under changing currents with time operating condition is established；Step S2 obtains changing currents with time operating condition
Under the power battery percentage of time run in different temperatures section and discharge in different depth of discharge sections time
Number percentage；Step S3, based on repeatedly circulation, by the percentage of time of each temperature range in the step S2 and each institute
The percentage of time for stating depth of discharge section substitutes into the Life Prediction Model under the correspondence operating condition in the step S1, calculates
To the bimetry of the power battery.
Further, described based on power battery under changing currents with time operating condition under single cycle in above-mentioned life-span prediction method
The function expression of Life Prediction Model is as follows：
Wherein, CyclesCFor based on it is a certain under single cycle when variable working condition under power battery bimetry, T is power
The running temperature of battery, DOD are the depth of discharge of power battery, CiFor discharge-rate of the power battery under i-th of operating condition,
RatioCiThe time scale of entire operating condition is accounted for for i-th of operating condition in a changing currents with time operating condition, i is the positive integer more than or equal to 1,
I-th of operating condition is expressed as (T, Ci, DOD)i。
Further, described based on power battery under changing currents with time operating condition under single cycle in above-mentioned life-span prediction method
The function expression of Life Prediction Model is obtained by following steps：
Sub-step S11 establishes power under single cycle using running temperature T, depth of discharge DOD and discharge-rate C as variable
The Life Prediction Model Cycles=f (T, C, DOD) of battery；Sub-step S12, variable working condition electric current when acquisition, according to the time-varying
The size of operating condition electric current converts to obtain the discharge-rate C at a certain momenti；Sub-step S13, for single cycle, depth of discharge DOD
It is constant, it is assumed that running temperature T is constant, is established by first discrete-method for integrating again based on a certain changing currents with time under single cycle
The bimetry model of power battery under operating condition
Further, described based on power under a certain changing currents with time operating condition under single cycle in above-mentioned life-span prediction method
Ratio in battery life predicting modelCiDetermination steps are as follows：Changing currents with time operating condition is separated into running temperature, depth of discharge
Under conditions of constant, the i composite condition that constant current discharges in preset time, i is the positive integer more than or equal to 1；Determine one
I-th operating condition (T, C in a changing currents with time operating conditioni, DOD)iThe time scale for accounting for entire operating condition is RatioCi=△ t/t, wherein
The time of one changing currents with time operating condition is t, each operating condition (T, Ci, DOD)iTime be △ t.
Further, in above-mentioned life-span prediction method, the expression formula Cycles=f of the power battery Life Prediction Model
(T, C, DOD) is polynomial form or exponential form.
Further, in above-mentioned life-span prediction method, the expression formula Cycles=f of the power battery Life Prediction Model
(T, C, DOD) is as follows：
Cycles=a0+a1*T+a2*DOD+a3*C+a4*(T-T0)*(DOD-DOD0)+a5*(T-T0)*(C-C0)+a6*C*
DOD+a7*(T-T0)*(T-T0)+a8*(DOD-DOD0)*(DOD-DOD0)+a9*(C-C0)*(C-C0)
In formula, cycle-index when Cycles is power battery capacity attenuation to initial capacity 80%, T is power battery
Running temperature, DOD are the depth of discharge of power battery, and C is the discharge-rate of power battery, a0、a1、a2、a3、a4、a5、a6、a7、
a8、a9、T0、C0And DOD0For fitting constant.
Further, in above-mentioned life-span prediction method, the power when discharge-rate, running temperature and constant depth of discharge
The expression formula Cycles=f (T, C, DOD) of battery life predicting model is as follows：
Cycle-index in formula, when Cycles is power battery capacity attenuation to initial capacity 80%；A0、b、Ea, c be quasi-
Close constant；R is free gas constant, R=8.314JK-1·mol-1；C is the discharge-rate of power battery.
Further, in above-mentioned life-span prediction method, the power battery prediction based on the lower corresponding operating condition of multiple circulation
The expression formula in service life is as follows：
Wherein, CyclescellFor the power battery bimetry based on the lower corresponding operating condition of multiple circulation, m, q, j are
Positive integer more than or equal to 1, TjFor the median of j-th of temperature range, RatioTjThe total moisture content is accounted for for j-th of temperature range
The percentage of time of data interval, DODqFor the median in q-th of depth of discharge section, RatioDODqFor q-th of depth of discharge area
Between account for the percentage of total discharge time, Cycles(Tj,DODq)It is based under single cycle for power battery, in a certain temperature
Between value bimetry under operating condition is corresponded to a certain depth of discharge median.
Further, in above-mentioned life-span prediction method, in the step S2, power battery is obtained from history charge data
Depth of discharge section.
Further, in above-mentioned life-span prediction method, each depth of discharge section DOD of the power battery(i)It indicates
For:
DOD(i)=SOCend(i-1)-SOCini(i), wherein i is the number of power battery charging, is the positive integer greater than 1,
SOCend(i-1)For the residual power percentage of (i-1)-th charge termination, SOCini(i)For the remaining capacity of i-th charging starting point
Percentage.
It is main with three that influence the power battery service life by the actual condition based on Vehicular dynamic battery in the present invention
Factor T, C, DOD are variable, in conjunction with the time variation of influence factor under automobile-used operating condition, first in the time scale of single cycle,
The Life Prediction Model of changing currents with time operating condition power battery is established, then in the time scale repeatedly recycled, considers running temperature
Influence with depth of discharge DOD to battery life finally obtains the when variable working condition power battery closer to actual use operating condition
Life model improves the accuracy of Vehicular dynamic battery life prediction, can be referred to by the calculated battery life of the model
Lead the use, maintenance and replacement of Vehicular dynamic battery, it is ensured that the safe handling of electric vehicle.In addition, the model is simply easy to count
It calculates, is conducive to improve forecasting efficiency.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field
Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention
Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings：
Fig. 1 be it is provided in an embodiment of the present invention when variable working condition under power battery life-span prediction method flow chart；
Fig. 2 is the chart that the battery core electric current for the NEDC operating condition real vehicle acquisition that example of the present invention provides changes over time；
Fig. 3 is the current discharge multiplying power change curve in the NEDC operating condition that example of the present invention provides；
Fig. 4 is the temperature variation curve in the NEDC operating condition that example of the present invention provides；
Fig. 5 is battery core running temperature distribution map in example of the present invention provide certain city operations vehicle 1 year；
Fig. 6 is that the power battery that provides of example of the present invention charges 1000 DOD ranges in actual use；
Fig. 7 is that the power battery that example of the present invention provides uses DOD in the distribution map in each section.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
It is fully disclosed to those skilled in the art.It should be noted that in the absence of conflict, embodiment in the present invention and
Feature in embodiment can be combined with each other.The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
Refering to fig. 1, the life-span prediction method of power battery includes following step under variable working condition when provided in an embodiment of the present invention
Suddenly：
Step S1 is based on single cycle, establishes the Life Prediction Model of power battery under changing currents with time operating condition.
Specifically, power battery can be lithium battery, lead-acid accumulator, nickel radical battery or sodium-sulphur battery etc., this implementation
Example is not limited in any way it.
Function expression based on power battery Life Prediction Model under changing currents with time operating condition under single cycle is as follows：
Wherein, CyclesCFor based on it is a certain under single cycle when variable working condition under power battery bimetry, T is power
The running temperature of battery, DOD are the depth of discharge of power battery, and Ci is power battery in i-th of work
Condition (T, Ci, DOD)iUnder discharge-rate, RatioCiFor i-th operating condition (T, C in a changing currents with time operating conditioni,
DOD)iThe time scale of entire operating condition is accounted for, i is the positive integer more than or equal to 1.
When it is implemented, establishing the Life Prediction Model of power battery under changing currents with time operating condition according to the following steps：
Sub-step S11 establishes power under single cycle using running temperature T, depth of discharge DOD and discharge-rate C as variable
The Life Prediction Model Cycles=f (T, C, DOD) of battery.
Specifically, the level of the use scope reasonable set (T, C, DOD) for Vehicular dynamic battery, for example, electric discharge
Depth DOD's may range from (60%~100%), and running temperature may range from (- 30~50) DEG C, discharge-rate C's
It may range from (0.5~5).
Intended when it is implemented, can choose with the data variation rule goodness of fit or the higher function expression of precision
It closes, obtains under constant (T, C, DOD) operating condition, the expression formula Cycles=f (T, C, DOD) of power battery Life Prediction Model can be with
For polynomial form or exponential form.Such as：
Cycles=a0+a1*T+a2*DOD+a3*C+a4*(T-T0)*(DOD-DOD0)+a5*(T-T0)*(C-C0)+a6*C*
DOD+a7*(T-T0)*(T-T0)+a8*(DOD-DOD0)*(DOD-DOD0)+a9*(C-C0)*(C-C0)
In formula, cycle-index when Cycles is power battery capacity attenuation to initial capacity 80%, T is power battery
Running temperature, DOD are the depth of discharge of power battery, and C is the discharge-rate of power battery, a0、a1、a2、a3、a4、a5、a6、a7、
a8、a9、T0、C0And DOD0For fitting constant, each fitting constant can be by experiment condition and lifetime data result according to response
Surface fitting obtains, that is, inputting any actual condition (T, C, DOD) can be obtained the corresponding service life.
The expression formula Cycles=of power battery Life Prediction Model when discharge-rate, running temperature and constant depth of discharge
F (T, C, DOD) also may indicate that as follows：
Cycle-index in formula, when Cycles is power battery capacity attenuation to initial capacity 80%；A0、b、Ea, c be quasi-
Constant is closed, each fitting constant can be fitted to obtain by experiment condition and lifetime data result according to response surface design；R is freely
Gas constant, R=8.314JK-1·mol-1；C is the discharge-rate of power battery.
Sub-step S12, variable working condition electric current when acquisition, according to it is described when variable working condition electric current size convert to obtain a certain moment
Discharge-rate Ci。
Specifically, the bimetry expression formula Cycles=f (T, C, DOD) obtained in sub-step S11 is suitable for constant
The life prediction of power battery under the conditions of discharge-rate C, running temperature T and depth of discharge DOD, and practical automobile-used operating condition is more multiple
Miscellaneous, size of current is becoming always in discharge process.Therefore, the present embodiment can be by real vehicle working condition acquiring electric current, or carries out
Emulation experiment simulates real vehicle operating condition, variable working condition electric current when acquisition.Power battery driving cycle can be the driving work of any rule
Condition, including but not limited to NEDC, EUDC, US06, HWFET, UDDS, US06 etc..
When it is implemented, can be by taking NEDC operating condition obtains the process of discharge-rate C as an example, as shown in Figure 2, it can be seen that putting
Size of current is becoming always in electric process, obtains the discharge-rate under the operating condition, i.e. C according to size of currenti=I/Cn, wherein I
It is size of current, unit A；CnFor battery rated capacity, unit Ah.
Sub-step S13, for single cycle, depth of discharge DOD is constant, it is assumed that running temperature T is constant, by first it is discrete-
The method integrated again establishes the life model based on power battery under changing currents with time operating condition a certain under single cycle
Specifically, the discrete method of changing currents with time operating condition can use time discrete, it is also possible to according to size of current
Using the methods of fuzzy logic, clustering.The method of time discrete is selected in the present embodiment.Based on certain a period of time under single cycle
The derivation process of the life model of power battery can specifically include following sub-step under time-dependent current operating condition：
First by changing currents with time operating condition be separated into running temperature, depth of discharge it is constant under conditions of, it is constant in preset time
I composite condition of current discharge, i are the positive integer more than or equal to 1.I composite condition is respectively (T, C1, DOD)1..., (T,
Ci, DOD)i。
Then determine a time for each operating condition (T, C in the changing currents with time operating condition of ti, DOD)iAccount for the entire operating condition time
Ratio is RatioCi=△ t/t, wherein each operating condition (T, Ci, DOD)iTime be △ t.
It is assumed that cell degradation does not have path dependence and memory effect, the circulation longevity under the operating condition is predicted by integration method
Life.
It is described in detail below with specific example and the longevity is predicted based on power battery under a certain changing currents with time operating condition under single cycle
The calculating process of life：
Electric current, set temperature and the DOD under demand operating condition are obtained first, wherein the setting of temperature and DOD take multiple circulation
Section intermediate value in step, the discharge-rate and temperature variation curve under a NEDC operating condition are as shown in Figure 3 and Figure 4.It can by Fig. 4
To find out, the variation of temperature is very small under a NEDC operating condition, it is therefore contemplated that under any NEDC operating condition,
Temperature is constant.
NEDC operating condition is simplified, mathematic(al) expectation after integral, specifically, by the time of NEDC operating condition according to 1298s in terms of,
For ease of calculation, operating condition is separated into 1298.That is i=1298, the corresponding time scale Ratio of each operating conditionCi=1/
1298, it in conjunction with Fig. 2, brings T, C, DOD data of corresponding points into f (T, C, DOD) model, obtains the service life under corresponding operating condition,
Service life of the battery under T, DOD condition NEDC operating condition can be obtained further according to following formula,
Such as T=25 DEG C, under DOD=0.9, i changes to 1298, C from 1iChange curve such as Fig. 3, by following formulaPower under this condition can be calculated
The cycle life of battery.
Similarly, T=45 DEG C, when DOD=0.8, the NEDC service life can calculate according to the following formula：
For ease of calculation, the embodiment of the present invention has carried out interval division to T and DOD, and with respective section intermediate value into
Row calculates, therefore, under the conditions of available different temperatures and depth of discharge, service life of the power battery in NEDC operating condition, the service life
As a result as shown in table 1 below：
The service life of constant T, DOD condition under 1 NEDC operating condition of table
| Temperature/DEG C | DOD/% | Service life/time |
| 20 | 80 | 4788 |
| 20 | 70 | 5116 |
| 25 | 80 | 4653 |
| 25 | 70 | 4752 |
| 30 | 80 | 4347 |
| 30 | 70 | 4283 |
Step S2 obtains the percentage of time that the power battery is run in different temperatures section under changing currents with time operating condition
And the percentage of time to discharge in different depth of discharge sections.
Specifically, the running temperature of battery can be obtained from battery management system BMS, according to demand by temperature range
Multiple wide temperature ranges are divided into, and calculate the ratio Ratio for each accounting for total moisture content rangeT, take section intermediate value TjAs generation
Table temperature is used for subsequent prediction battery life.For example, the distribution of battery core running temperature is such as Fig. 5 institute in certain city operations vehicle 1 year
Show, such as temperature range (10-30) DEG C, its percentage of time for accounting for total moisture content data can be calculated, and take intermediate value 20
DEG C as represent temperature for the subsequent service life calculating.Demarcation interval simultaneously takes intermediate value as calculating is represented, and is to comprehensively consider knot
The selection made in the case where fruit precision and calculation amount advantageously reduces calculation amount and quickly obtains Vehicular battery cycle life
Predicted value.
Depth of discharge DOD can be obtained from historical data, historical data may come from same vehicle, can be from certain
One vehicle can be from a certain vehicle from different places, can satisfy the forecast demand of different levels.Specifically, depth of discharge
Data can come from the T-box data of vehicle upload, be also possible to the record data of charging pile.When it is implemented, obtaining electric discharge
The method of depth DOD is：By recording each initial, the termination SOC point that charges, it can be calculated user's Vehicular battery and use every time
The section DOD, each depth of discharge section DOD of power battery(i)It is expressed as:
DOD(i)=SOCend(i-1)-SOCini(i), wherein i is the number of power battery charging, is the positive integer greater than 1,
SOCend(i-1)For the residual power percentage of (i-1)-th charge termination, SOCini(i)For the remaining capacity of i-th charging starting point
Percentage.For each depth of discharge section following table 2 of power battery：
The each depth of discharge section of 2 power battery of table
The depth of discharge DOD of 1000 charge and discharge of user actual use is as shown in fig. 6, refering to Fig. 7, by depth of discharge DOD
Multiple wide sections DOD are divided into, calculate battery using DOD in each section distribution proportion RatioDOD, take section intermediate value DODq
Subsequent prediction battery life is used for as depth of discharge DOD is represented.
Step S3, based on repeatedly circulation, by the percentage of time of each temperature range in above-mentioned steps 2 and each described
The percentage of time in depth of discharge section substitutes into the Life Prediction Model under the correspondence operating condition in step 1, is calculated described
The bimetry of power battery.
Specifically, the expression formula of the power battery bimetry based on the lower corresponding operating condition of multiple circulation is as follows：
Wherein, CyclescellFor the power battery bimetry based on the lower corresponding operating condition of multiple circulation, m, q, j are
Positive integer more than or equal to 1, TjFor the median of j-th of temperature range, RatioTjThe total moisture content is accounted for for j-th of temperature range
The percentage of time of data interval, DODqFor the median in q-th of depth of discharge section, RatioDODqIt is accounted for for q-th of temperature range
The percentage of total discharge time, Cycles(Tj,DODq)It is based under single cycle for power battery, in a certain temperature median
The bimetry under operating condition is corresponded to a certain depth of discharge median.
When the percentage of time of the percentage of time of running temperature T and depth of discharge DOD is as shown in table 3 below, in conjunction with table 1
In data can be calculated the power battery bimetry of the repeatedly lower corresponding operating condition of circulation.
3 running temperature T of table and depth of discharge DOD ratio
| Temperature/DEG C | Ratio | DOD | Ratio |
| 20 | 0.8 | 0.8 | 0.1 |
| 25 | 0.1 | 0.7 | 0.9 |
| 30 | 0.1 |
The calculating process in power battery service life is：
Cyclescell=4788*0.8*0.1+5116*0.8*0.9+4653*0.1*0.1+4752*0.1*0.9+4347 *
0.1*0.1+4283*0.1*0.9=4969 is 4969 times to get the cycle life of the power battery arrived.
It is above-mentioned obviously it can be concluded that, provided in the present embodiment when variable working condition under power battery life-span prediction method, base
In the actual condition of Vehicular dynamic battery, to influence three principal elements T, C, the DOD in power battery service life as variable, in conjunction with vehicle
Changing currents with time operating condition power battery is established first in the time scale of single cycle with the time variation of influence factor under operating condition
Life Prediction Model consider running temperature and depth of discharge DOD to battery life then in the time scale repeatedly recycled
Influence, finally obtain closer to actual use operating condition when variable working condition power battery life model, improve power train in vehicle application electricity
The accuracy of pond life prediction can instruct use, the maintenance of Vehicular dynamic battery by the calculated battery life of the model
And replacement, it is ensured that the safe handling of electric vehicle.In addition, the model is simply easy to calculate, be conducive to improve forecasting efficiency.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (10)
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| CN109598382B (en) * | 2018-12-05 | 2021-02-23 | 潍柴动力股份有限公司 | A kind of life prediction method and device |
| CN109669145A (en) * | 2018-12-24 | 2019-04-23 | 湖南科霸汽车动力电池有限责任公司 | Vehicle-mounted nickel-metal hydride battery packet SOH evaluation method |
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| CN111610459B (en) * | 2019-02-25 | 2024-05-28 | 丰田研究所股份有限公司 | System, method and storage medium for predicting discharge curve of battery pack |
| WO2020233544A1 (en) * | 2019-05-20 | 2020-11-26 | Oppo广东移动通信有限公司 | Battery test system and method |
| CN112014735A (en) * | 2019-05-30 | 2020-12-01 | 上海汽车集团股份有限公司 | Battery cell aging life prediction method and device based on full life cycle |
| CN112014735B (en) * | 2019-05-30 | 2023-09-22 | 上海汽车集团股份有限公司 | Battery cell aging life prediction method and device based on full life cycle |
| CN110376526A (en) * | 2019-07-08 | 2019-10-25 | 南京航空航天大学 | A kind of SOC estimation method of high-altitude flight environment unmanned plane power battery |
| CN110376526B (en) * | 2019-07-08 | 2021-05-11 | 南京航空航天大学 | SOC estimation method of unmanned aerial vehicle power battery in high-altitude flight environment |
| CN110470991A (en) * | 2019-08-23 | 2019-11-19 | 江西优特汽车技术有限公司 | A kind of power battery cycle life evaluating method |
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| CN110750874A (en) * | 2019-09-26 | 2020-02-04 | 长沙理工大学 | A method for life prediction of retired power battery |
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| US11408942B2 (en) | 2019-09-26 | 2022-08-09 | Changsha University Of Science & Technology | Method for predicting service life of retired power battery |
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| CN114047452B (en) * | 2022-01-13 | 2022-05-13 | 浙江玥视科技有限公司 | Method and device for determining cycle life of battery |
| CN114415050A (en) * | 2022-01-14 | 2022-04-29 | 北京理工大学 | An online quantification method of power battery life decline |
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| CN117572243B (en) * | 2023-11-08 | 2024-06-04 | 北京天易数聚科技有限公司 | Method and system for predicting cycle life of energy storage battery |
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