CN108919129B - Service life prediction method of power battery under time-varying working condition - Google Patents
Service life prediction method of power battery under time-varying working condition Download PDFInfo
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Abstract
The invention provides a method for predicting the service life of a power battery under a time-varying working condition, which comprises the following steps: based on the actual working condition of the power battery for the vehicle, three main factors T, C, DOD influencing the service life of the power battery are used as variables, and the time-varying property of the influencing factors under the working condition for the vehicle is combined, firstly, a service life prediction model of the power battery under the time-varying current working condition is established in the time scale of single circulation, and then, the influence of the operating temperature and the DOD on the service life of the power battery is considered in the time scale of multiple circulation, so that the service life model of the power battery under the time-varying working condition closer to the actual working condition is finally obtained, the accuracy of service life prediction of the power battery for the vehicle is improved, the service life of the power battery for the vehicle can be guided through the service life of the power battery calculated by the. In addition, the model is simple and easy to calculate, and is beneficial to improving the prediction efficiency.
Description
Technical Field
The invention relates to the technical field of power batteries, in particular to a method for predicting the service life of a power battery under a time-varying working condition.
Background
In recent years, research on new energy automobiles is actively carried out in various countries, and lithium ion batteries are increasingly applied as driving energy in the field of power batteries due to the characteristics of high energy density, high working voltage, long cycle life, low self-discharge rate, no memory effect and the like.
The development process of the lithium ion power battery comprises the aspects of electrical property, core function, service life, safety and the like, and the service life development is the most important aspect. During the use of the battery, the aging of the battery is influenced by various stresses, including ambient temperature, humidity, mechanical pressure, radiation, current, voltage, SOC range and the like. Among the many factors, the stresses that have a major effect on the aging of the battery are ambient temperature and during its use (rate, DOD). During the use process, the capacity and the internal resistance of the battery can change, and the law of the capacity attenuation or the internal resistance increase of the battery is generally used for representing and predicting the service life of the battery.
In order to obtain the service life data of the battery, most lithium ion battery aging researches are carried out based on laboratory working conditions, stress is kept constant along with time, for example, at a certain set temperature, constant current \ constant current-constant voltage charging and discharging are carried out, a service life model is established, and the relation between the stress and the service life change is analyzed. However, the working conditions of the vehicle are various in alternation, the temperature and the current usually change along with time, especially the current, even rapidly changes, and therefore the battery aging behavior under the actual working condition cannot be directly predicted based on the service life model established under the laboratory working condition.
Disclosure of Invention
In view of the above, the invention provides a method for predicting the service life of a power battery under a time-varying working condition, and aims to solve the problem that the accuracy of a prediction result is low due to the fact that an existing power battery service life prediction model does not conform to an actual use working condition.
The invention provides a method for predicting the service life of a power battery under a time-varying working condition, which comprises the following steps of S1, establishing a service life prediction model of the power battery under the time-varying current working condition based on single circulation; step S2, acquiring the percentage of the operation time of the power battery in different temperature intervals and the percentage of the discharge times in different discharge depth intervals under the working condition of time-varying current; and step S3, based on multiple cycles, substituting the time percentage of each temperature interval and the number percentage of each depth of discharge interval in step S2 into the life prediction model under the corresponding working condition in step S1, and calculating the predicted life of the power battery.
Further, in the above method for predicting the service life, the functional expression of the model for predicting the service life of the power battery based on the time-varying current under the single cycle is as follows:
wherein, CyclesCFor predicting the service life of the power battery under a certain time-varying working condition based on a single cycle, T is the operating temperature of the power battery, DOD is the discharge depth of the power battery, and CiIs the discharge rate, Ratio, of the power battery under the ith working conditionCiIs the time proportion of the ith working condition in the time-varying current working condition to the whole working condition, i is a positive integer greater than or equal to 1, and the ith working condition is expressed as (T, C)i，DOD)i。
Further, in the above method for predicting the service life, the functional expression of the model for predicting the service life of the power battery based on the time-varying current under the single-cycle condition is obtained by the following steps:
a substep S11, establishing a life prediction model cycle of the power battery under a single cycle by taking the operating temperature T, the depth of discharge DOD and the discharge rate C as variables, wherein the life prediction model cycle is f (T, C, DOD); and a substep S12 of obtaining time-varying working condition current, and obtaining the discharge multiplying factor C of a certain moment through conversion according to the magnitude of the time-varying working condition currenti(ii) a And a substep S13, establishing a life prediction model based on the power battery under a certain time variable current working condition under the single cycle by a method of firstly dispersing and then integrating for the single cycle with constant DOD (depth of discharge), and assuming that the operating temperature T is constant
Further, in the service life prediction method, the Ratio in the power battery service life prediction model based on a certain time-varying current working condition under a single cycleCiThe determination steps are as follows: dispersing the working conditions of the time-varying current into i combined working conditions of constant current discharge in preset time under the condition of constant operating temperature and constant discharge depth, wherein i is a positive integer greater than or equal to 1; determining the ith operating mode (T, C) within a time-varying current operating modei，DOD)iThe time Ratio of the whole working condition is RatioCiΔ T/T, where the time of one time-varying current regime is T, each regime (T, C)i，DOD)iIs Δ t.
Further, in the above life prediction method, the expression cycle ═ f (T, C, DOD) of the power battery life prediction model is in a polynomial form or an exponential form.
Further, in the above life prediction method, the expression cycle ═ f (T, C, DOD) of the power battery life prediction model 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)
wherein Cycles is the cycle number when the capacity of the power battery is attenuated to 80 percent of the initial capacity, T is the operating temperature of the power battery, DOD is the discharge depth of the power battery, C is the discharge rate of the power battery, a0、a1、a2、a3、a4、a5、a6、a7、a8、a9、T0、C0And DOD0Is a fitting constant.
Further, in the above life prediction method, the expression Cycles ═ f (T, C, DOD) of the power battery life prediction model when the discharge rate, the operating temperature, and the depth of discharge are constant is as follows:
wherein, Cycles is the cycle number when the capacity of the power battery is attenuated to 80 percent of the initial capacity; a. the0、b、EaC is a fitting constant; r is the free gas constant, R is 8.314 J.K-1·mol-1(ii) a And C is the discharge rate of the power battery.
Further, in the above life prediction method, the expression of the predicted life of the power battery based on the corresponding working conditions under multiple cycles is as follows:
wherein, CyclescellPredicting the service life of the power battery based on the corresponding working conditions under multiple cycles, wherein m, q and j are positive integers greater than or equal to 1, and TjIs the middle value of the jth temperature interval, RatioTjIs the time percentage of the jth temperature interval in the total temperature data interval, DODqIs the middle value of the q-th depth-of-discharge interval, RatioDODqCycles, which is the percentage of the q-th discharge depth interval to the total discharge times(Tj,DODq)The method is used for predicting the service life of the power battery under the working condition corresponding to a certain temperature intermediate value and a certain discharge depth intermediate value under the condition of single circulation.
Further, in the above life prediction method, in step S2, the discharge depth section of the power battery is acquired from the historical charge data.
Further, in the above method for predicting a lifetime, the depth of discharge interval DOD of the power battery is determined every time(i)Expressed as:
DOD(i)＝SOCend(i-1)-SOCini(i)wherein i is the charging frequency of the power battery, which is a positive integer greater than 1, SOCend(i-1)Percentage of remaining charge at i-1 time end of charge, SOCini(i)Is the percentage of the remaining capacity at the ith charge starting point.
According to the invention, based on the actual working condition of the power battery for the vehicle, three main factors T, C, DOD influencing the service life of the power battery are taken as variables, and the time-varying property of the influencing factors under the working condition of the vehicle is combined, firstly, a service life prediction model of the power battery under the time-varying current working condition is established within the time scale of single circulation, and then, the influence of the operating temperature and the DOD on the service life of the power battery under the time scale of multiple circulation is considered, so that the service life model of the power battery under the time-varying working condition closer to the actual working condition is finally obtained, the accuracy of service life prediction of the power battery for the vehicle is improved, the service, maintenance and replacement of the power battery for the vehicle can be guided through the service life of the. In addition, the model is simple and easy to calculate, and is beneficial to improving the prediction efficiency.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of a method for predicting a service life of a power battery under a time-varying working condition according to an embodiment of the present invention;
fig. 2 is a graph of a change of a cell current acquired in a real vehicle under the NEDC working condition according to an embodiment of the present invention with time;
fig. 3 is a current discharge rate variation curve in the primary NEDC operating condition according to an embodiment of the present invention;
FIG. 4 is a temperature variation curve in a primary NEDC operating mode according to an embodiment of the present invention;
fig. 5 is a diagram illustrating a distribution of cell operating temperatures of a certain urban operating vehicle over a year according to an embodiment of the present invention;
fig. 6 shows the DOD range of 1000 charges in practical use of the power battery according to the embodiment of the present invention;
fig. 7 is a distribution diagram of DOD used by the power battery in each interval according to the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, 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 embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, a method for predicting the service life of a power battery under a time-varying working condition according to an embodiment of the present invention includes the following steps:
and step S1, establishing a life prediction model of the power battery under the working condition of time-varying current based on a single cycle.
Specifically, the power battery may be a lithium battery, a lead-acid battery, a nickel-based battery, a sodium-sulfur battery, or the like, and the present embodiment does not limit the power battery in any way.
The function expression of the power battery service life prediction model under the working condition of time-varying current under single cycle is as follows:
wherein, CyclesCFor predicting the service life of the power battery under a certain time-varying working condition based on a single cycle, T is the operating temperature of the power battery, DOD is the discharge depth of the power battery, and Ci is the power battery in the ith work station
Condition (T, C)i，DOD)iDischarge rate ofCiIs the ith working condition (T, C) in a time-varying current working conditioni，DOD)iAnd i is a positive integer greater than or equal to 1 according to the time proportion of the whole working condition.
In specific implementation, a life prediction model of the power battery under the working condition of time-varying current is established according to the following steps:
and a substep S11, establishing a life prediction model cycle of the power battery under a single cycle as f (T, C, DOD) by taking the operating temperature T, the depth of discharge DOD and the discharge rate C as variables.
Specifically, the level of (T, C, DOD) is set reasonably for the use range of the vehicle power battery, for example, the range of the depth of discharge DOD can be (60% -100%), the range of the operating temperature can be (-30-50) DEG C, and the range of the discharge multiplying power C can be (0.5-5).
During specific implementation, a function expression with high data change rule goodness of fit or high precision can be selected for fitting, and under the working condition of obtaining constant (T, C and DOD), the expression cycle (f (T, C and DOD) of the power battery life prediction model can be in a polynomial form or an exponential form. For example:
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 the formulaThe Cycles is the cycle number when the capacity of the power battery is attenuated to 80 percent of the initial capacity, T is the operating temperature of the power battery, DOD is the discharge depth of the power battery, C is the discharge multiplying power of the power battery, a0、a1、a2、a3、a4、a5、a6、a7、a8、a9、T0、C0And DOD0The fitting constants are obtained by fitting experimental conditions and life data results according to a response surface, namely, the corresponding life can be obtained by inputting any actual condition (T, C, DOD).
The expression Cycles ═ f (T, C, DOD) of the power battery life prediction model at constant discharge rate, operating temperature and depth of discharge can also be expressed as follows:
wherein, Cycles is the cycle number when the capacity of the power battery is attenuated to 80 percent of the initial capacity; a. the0、b、EaC is a fitting constant, and each fitting constant can be obtained by fitting an experimental working condition and a service life data result according to a response surface; r is the free gas constant, R is 8.314 J.K-1·mol-1(ii) a And C is the discharge rate of the power battery.
And a substep S12 of obtaining time-varying working condition current, and obtaining the discharge multiplying factor C of a certain moment through conversion according to the magnitude of the time-varying working condition currenti。
Specifically, the predicted life expression cycle ═ f (T, C, DOD) obtained in the sub-step S11 is suitable for predicting the life of the power battery under the conditions of constant discharge rate C, operating temperature T and depth of discharge DOD, and the actual vehicle operating conditions are complex and the current magnitude is constantly changing during the discharge process. Therefore, the current can be collected through the working condition of the real vehicle, or the simulation experiment is carried out to simulate the working condition of the real vehicle, so as to obtain the current under the time-varying working condition. The driving condition of the power battery can be any regular driving condition, including but not limited to NEDC, EUDC, US06, HWFET, UDDS, US06, and the like.
In specific implementation, the process of obtaining the discharge rate C under the NEDC condition is taken as an example, as shown in fig. 2, it can be seen that the current magnitude is constantly changed in the discharge process, and the discharge rate under the condition, i.e., C, is obtained according to the current magnitudei＝I/CnWherein I is the current magnitude and has the unit of A; cnThe unit is the rated capacity of the battery and is Ah.
And a substep S13, establishing a life model based on the power battery under a certain time variable current working condition under the single cycle by a method of firstly dispersing and then integrating for the single cycle with constant depth of discharge DOD and assuming that the operating temperature T is constant
Specifically, the time-varying current working condition may be discrete time, or may be fuzzy logic, cluster analysis, or the like according to the magnitude of the current. In this embodiment, a time dispersion method is selected. The derivation process of the life model of the power battery under a certain time-varying current condition based on a single cycle may specifically include the following sub-steps:
firstly, dispersing the working condition of time-varying current into i combined working conditions of constant current discharge in preset time under the condition of constant operating temperature and constant discharge depth, wherein i is a positive integer greater than or equal to 1. The combined working conditions of the i groups are respectively (T, C)1，DOD)1、…、(T，Ci，DOD)i。
Then, the respective operating mode (T, C) of a time-varying current operating mode with time T is determinedi，DOD)iThe Ratio of the total working time is RatioCiΔ T/T, wherein each operating mode (T, C)i，DOD)iIs Δ t.
Assuming that the battery aging has no path dependence and memory effect, the cycle life under the working condition is predicted by an integral method.
The following detailed description is provided as a specific example of the calculation process of the predicted service life of the power battery under a certain time-varying current working condition based on a single cycle:
firstly, the current, the set temperature and the DOD under the required working condition are obtained, wherein the setting of the temperature and the DOD is the interval median value in the multiple circulation steps, and the discharge rate and temperature change curve under the one-time NEDC working condition is shown in fig. 3 and 4. As can be seen from fig. 4, the temperature changes very slightly in one NEDC condition, and therefore, it can be considered that the temperature is constant in any one NEDC condition.
The method is characterized in that the NEDC working condition is simplified, the service life is calculated after integration, specifically, the time of the NEDC working condition is calculated according to 1298s, and the working condition is separated into 1298 working conditions for convenience of calculation. I.e. 1298, the time Ratio for each operating mode Ci1/1298, and with reference to fig. 2, substituting T, C, DOD data of the corresponding point into the f (T, C, DOD) model to obtain the service life under the corresponding condition, and obtaining the service life of the battery under the T, DOD condition NEDC condition according to the following formula,
e.g. i varies from 1 to 1298, C, at 25 ℃, DOD 0.9iThe variation curve is shown in FIG. 3 and is represented by the following formula
The cycle life of the power cell under this condition can be calculated.
Similarly, when T is 45 ℃ and DOD is 0.8, the NEDC lifetime can be calculated as follows:
in order to facilitate calculation, in the embodiment of the present invention, T and DOD are divided into intervals, and the respective interval median values are used for calculation, so that the service life of the power battery under different temperature and depth of discharge conditions in the NEDC operating condition can be obtained, and the service life results are shown in table 1 below:
TABLE 1 Life of constant T, DOD condition under NEDC conditions
| Temperature/. degree.C | DOD/％ | Life/ |
| 20 | 80 | 4788 |
| 20 | 70 | 5116 |
| 25 | 80 | 4653 |
| 25 | 70 | 4752 |
| 30 | 80 | 4347 |
| 30 | 70 | 4283 |
And step S2, acquiring the percentage of the operation time of the power battery in different temperature intervals and the percentage of the discharge times in different discharge depth intervals under the working condition of time-varying current.
In particular, power may be taken from the battery management system BMSDividing the temperature range into a plurality of temperature intervals with equal width according to the requirement of the running temperature of the pool, and calculating the Ratio of each temperature interval in the total temperature rangeTTaking the median value T of intervaljAs a representative temperature for subsequent prediction of battery life. For example, the cell operating temperature distribution of a certain city operating vehicle in one year is shown in fig. 5, for example, for a temperature interval (10-30) deg.c, the time percentage of the cell operating temperature distribution in the total temperature data can be calculated, and the median 20 deg.c is taken as a representative temperature for the subsequent life calculation. The interval is divided, the median value is taken as a representative calculation, the calculation is selected under the condition of comprehensively considering the result precision and the calculated amount, and the calculation amount is favorably reduced, and the predicted value of the cycle life of the vehicle battery is quickly obtained.
The DOD can be obtained from historical data, the historical data can be from the same vehicle, can also be from a certain vehicle type, can also be from different vehicles in a certain area, and can meet the prediction requirements of different levels. Specifically, the depth of discharge data can be T-box data uploaded by a vehicle, and can also be recorded data of a charging pile. In specific implementation, the method for obtaining the DOD comprises the following steps: the DOD interval of each use of the vehicle battery of the user and the DOD interval of each discharge depth of the power battery can be obtained by calculating through recording the initial and ending SOC points of each charge(i)Expressed as:
DOD(i)＝SOCend(i-1)-SOCini(i)wherein i is the charging frequency of the power battery, which is a positive integer greater than 1, SOCend(i-1)Percentage of remaining charge at i-1 time end of charge, SOCini(i)Is the percentage of the remaining capacity at the ith charge starting point. The discharge depth interval of the power battery at each time is as an example in the following table 2:
TABLE 2 depth of discharge interval of power battery each time
The DOD (depth of discharge) actually used by a user in 1000 charge and discharge cycles is shown in FIG. 6, and referring to FIG. 7, the DOD is divided into a plurality of DOD intervals with equal widthCalculating the distribution Ratio of the battery use DOD in each intervalDODTaking the median DOD of intervalqAs a representative depth of discharge DOD for subsequent prediction of battery life.
And step S3, substituting the time percentage of each temperature interval and the number percentage of each depth-of-discharge interval in the step 2 into the life prediction model under the corresponding working condition in the step 1 based on multiple cycles, and calculating to obtain the predicted life of the power battery.
Specifically, the expression of the predicted service life of the power battery based on the corresponding working condition under multiple cycles is as follows:
wherein, CyclescellPredicting the service life of the power battery based on the corresponding working conditions under multiple cycles, wherein m, q and j are positive integers greater than or equal to 1, and TjIs the middle value of the jth temperature interval, RatioTjIs the time percentage of the jth temperature interval in the total temperature data interval, DODqIs the middle value of the q-th depth-of-discharge interval, RatioDODqCycles being the percentage of the qth temperature interval over the total number of discharges(Tj,DODq)The method is used for predicting the service life of the power battery under the working condition corresponding to a certain temperature intermediate value and a certain discharge depth intermediate value under the condition of single circulation.
When the time percentage of the operating temperature T and the time percentage of the depth of discharge DOD are shown in the following table 3, the predicted service life of the power battery under the corresponding working condition under multiple cycles can be obtained by combining the data calculation in the table 1.
TABLE 3 operating temperature T and DOD ratio of depth of discharge
| Temperature/. degree.C | Ratio of | DOD | Ratio of |
| 20 | 0.8 | 0.8 | 0.1 |
| 25 | 0.1 | 0.7 | 0.9 |
| 30 | 0.1 |
The calculation process of the service life of the power battery is as follows:
Cyclescell4788 × 0.8 × 0.1+5116 × 0.8 × 0.9+4653 × 0.1+4752 × 0.1 × 0.9+4347 × 0.1+4283 × 0.1 × 0.9 ═ 4969, i.e., the cycle life of the resulting power cell was 4969.
It can be obviously obtained from the above description that, the method for predicting the service life of the power battery under the time-varying working condition provided in this embodiment is based on the actual working condition of the power battery for the vehicle, and takes three main factors T, C, DOD affecting the service life of the power battery as variables, and combines the time-varying property of the affecting factors under the vehicle working condition, firstly, in the time scale of a single cycle, a service life prediction model of the power battery under the time-varying current working condition is established, and then, in the time scale of multiple cycles, the influence of the operating temperature and the discharging depth DOD on the service life of the battery is considered, so as to finally obtain the service life model of the power battery under the time-varying working condition closer to the actual working condition, so that the accuracy of service life prediction of the power battery for the vehicle is improved, and the service life of the. In addition, the model is simple and easy to calculate, and is beneficial to improving the prediction efficiency.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method for predicting the service life of a power battery under a time-varying working condition is characterized by comprising the following steps:
step S1, establishing a life prediction model of the power battery under the working condition of time-varying current based on single circulation; the function expression of the power battery service life prediction model under the working condition of time-varying current under single cycle is as follows:
wherein, CyclesCFor predicting the service life of the power battery under a certain time-varying working condition based on a single cycle, T is the operating temperature of the power battery, DOD is the discharge depth of the power battery, and CiIs the discharge rate, Ratio, of the power battery under the ith working conditionCiIs the time proportion of the ith working condition in the time-varying current working condition to the whole working condition, i is a positive integer greater than or equal to 1, and the ith working condition is expressed as (T, C)i，DOD)i；
Step S2, acquiring the percentage of the operation time of the power battery in different temperature intervals and the percentage of the discharge times in different discharge depth intervals under the working condition of time-varying current;
and step S3, based on multiple cycles, substituting the time percentage of each temperature interval and the number percentage of each depth of discharge interval in step S2 into the life prediction model under the corresponding working condition in step S1, and calculating the predicted life of the power battery.
2. The life prediction method of claim 1, wherein the functional expression based on the life prediction model of the power battery under the time-varying current working condition under a single cycle is obtained by the following steps:
a substep S11, establishing a life prediction model cycle of the power battery under a single cycle by taking the operating temperature T, the depth of discharge DOD and the discharge rate C as variables, wherein the life prediction model cycle is f (T, C, DOD);
and a substep S12 of obtaining time-varying working condition current, and obtaining the discharge multiplying factor C of a certain moment through conversion according to the magnitude of the time-varying working condition currenti；
And a substep S13, establishing a life prediction model based on the power battery under a certain time variable current working condition under the single cycle by a method of firstly dispersing and then integrating for the single cycle with constant DOD (depth of discharge), and assuming that the operating temperature T is constant
3. The life prediction method according to claim 1 or 2, wherein the Ratio in the power battery life prediction model based on a certain time-varying current working condition in a single cycleCiThe determination steps are as follows:
dispersing the working conditions of the time-varying current into i combined working conditions of constant current discharge in preset time under the condition of constant operating temperature and constant discharge depth, wherein i is a positive integer greater than or equal to 1;
determining the respective operating conditions (T, C) within a time-varying current operating conditioni，DOD)iThe time Ratio of the whole working condition is RatioCiΔ T/T, where the time of one time-varying current regime is T, each regime (T, C)i，DOD)iIs Δ t.
4. The life prediction method according to claim 2, wherein the expression Cycles ═ f (T, C, DOD) of the power battery life prediction model is in a polynomial form or an exponential form.
5. The life prediction method according to claim 4, wherein the expression Cycles ═ f (T, C, DOD) of the power battery life prediction model 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)
wherein Cycles is the cycle number when the capacity of the power battery is attenuated to 80 percent of the initial capacity, T is the operating temperature of the power battery, DOD is the discharge depth of the power battery, C is the discharge rate of the power battery, a0、a1、a2、a3、a4、a5、a6、a7、a8、a9、T0、C0And DOD0Is a fitting constant.
6. The life prediction method according to claim 4, wherein the expression Cycles ═ f (T, C, DOD) of the power battery life prediction model at a constant discharge rate, operating temperature, and depth of discharge is as follows:
wherein Cycles is the number of Cycles when the power battery capacity decays to 80% of the initial capacity, A0、b、EaC is a fitting constant; r is the free gas constant, R is 8.314 J.K-1·mol-1(ii) a And C is the discharge rate of the power battery.
7. The life prediction method according to claim 2, wherein the expression of the predicted life of the power battery based on the corresponding working conditions under multiple cycles is as follows:
wherein, CyclescellPredicting the service life of the power battery based on the corresponding working conditions under multiple cycles, wherein q and j are positive integers greater than or equal to 1, and TjIs the middle value of the jth temperature interval, RatioTjDOD as a percentage of time that the jth temperature interval occupies the total temperature data intervalqIs the middle value of the q-th depth-of-discharge interval, RatioDODqCycles, which is the percentage of the q-th discharge depth interval to the total discharge times(Tj，DODq)The method is used for predicting the service life of the power battery under the working condition corresponding to a certain temperature intermediate value and a certain discharge depth intermediate value under the condition of single circulation.
8. The method for predicting the life of a power battery according to claim 1, wherein in step S2, the discharge depth interval of the power battery is obtained from historical charging data.
9. The method of claim 6, wherein the power battery has a DOD range of depth of discharge per pass(i)Expressed as:
DOD(i)＝SOCend(i-1)-SOCini(i)wherein i is the charging frequency of the power battery, which is a positive integer greater than 1, SOCend(i-1)Percentage of remaining charge at i-1 time end of charge, SOCini(i)Is the percentage of the remaining capacity at the ith charge starting point.
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Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109598382B (en) * | 2018-12-05 | 2021-02-23 | 潍柴动力股份有限公司 | Service life prediction method and device |
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| CN114295998B (en) * | 2021-12-28 | 2024-07-09 | 东软睿驰汽车技术（沈阳）有限公司 | Method, device, equipment and storage medium for predicting service life of power battery |
| 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 |
| 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 |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102362190A (en) * | 2009-03-24 | 2012-02-22 | 美国能量变换公司 | Battery life estimation |
| CN102520367A (en) * | 2011-12-26 | 2012-06-27 | 中国电子科技集团公司第十八研究所 | Method for evaluating life of space hydrogen-nickel storage batteries |
| CN103698710A (en) * | 2013-12-12 | 2014-04-02 | 中南大学 | Prediction method for life cycle of battery |
| CN106291372A (en) * | 2016-07-22 | 2017-01-04 | 南京工业大学 | Novel method for predicting residual life of lithium ion power battery |
| CN107015157A (en) * | 2017-04-01 | 2017-08-04 | 湖南银杏数据科技有限公司 | The lithium battery cycles left life-span online fast test method of fragment is risen based on constant current equipressure |
| KR20170139973A (en) * | 2016-06-10 | 2017-12-20 | 주식회사 엘지화학 | Apparatus and method for counting a cycle of a battery |
| CN107894571A (en) * | 2017-11-06 | 2018-04-10 | 北京长城华冠汽车科技股份有限公司 | On-vehicle battery group life estimation method |
-
2018
- 2018-06-08 CN CN201810588556.5A patent/CN108919129B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102362190A (en) * | 2009-03-24 | 2012-02-22 | 美国能量变换公司 | Battery life estimation |
| CN102520367A (en) * | 2011-12-26 | 2012-06-27 | 中国电子科技集团公司第十八研究所 | Method for evaluating life of space hydrogen-nickel storage batteries |
| CN103698710A (en) * | 2013-12-12 | 2014-04-02 | 中南大学 | Prediction method for life cycle of battery |
| KR20170139973A (en) * | 2016-06-10 | 2017-12-20 | 주식회사 엘지화학 | Apparatus and method for counting a cycle of a battery |
| CN106291372A (en) * | 2016-07-22 | 2017-01-04 | 南京工业大学 | Novel method for predicting residual life of lithium ion power battery |
| CN107015157A (en) * | 2017-04-01 | 2017-08-04 | 湖南银杏数据科技有限公司 | The lithium battery cycles left life-span online fast test method of fragment is risen based on constant current equipressure |
| CN107894571A (en) * | 2017-11-06 | 2018-04-10 | 北京长城华冠汽车科技股份有限公司 | On-vehicle battery group life estimation method |
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